InfoSymbiotics/DDDAS: The Power of Dynamic Data Driven Applications Systems

Report of the
August 2010 Multi-Agency Workshop on

InfoSymbiotics/DDDAS
The Power of Dynamic Data Driven Applications Systems

Workshop Sponsored by:
Air Force Office of Scientific Research
and
National Science Foundation

Workshop Co-Chairs

Craig Douglas, University of Wyoming
Abani Patra, University at Buffalo, SUNY

Principal Sponsoring Agency Liaisons
Frederica Darema, AFOSR
H. Ed Seidel, NSF

Working Group Leads
Gabrielle Allen, LSU; George Biros, Georgia Tech; Janice Coen, NCAR; Alok Chaturvedi, Purdue;
Shantenu Jha, Rutgers; Bani Mallick, Texas A&M; Dinesh Manocha, NCSU;
Adrian Sandhu, Virginia Tech; Srinidhi Varadarajan, Virginia Tech; Dongbin Xiu, Purdue

Workshop Award Contract Managers
Robert Bonneau, AFOSR
Manish Parashar, NSF

Cross-Agencies Committee

DOD/AFOSR: NSF:
F. Darema H. E. Seidel (MPS)
R. Bonneau J. Cherniavsky (EHR)
F. Fahroo T. Henderson (CISE)
K. Reinhardt L. Jameson (MPS)
D. Stargel G. Maracas (ENG)
DOD/ONR: Ralph Wachter M. Parashar (OCI)
DOD/ARL/CIS: Ananthram Swami
DOD/DTRA: Kiki Ikossi NIH:
Milt Corn (NLM),
NASA: Michael Seablom Peter Lyster (NIGMS)

Acknowledgements
We acknowledge the support of the Air Force Office of Scientific Research under Contract no. FA9550-
10-1-0477 and the National Science Foundation under Award number OCI-1057753.
The administrative support of the Center for Computational Research at University at Buffalo is
sincerely appreciated.

Executive Summary

InfoSymbioticSystems/InfoSymbiotics embody the power of the Dynamic Data Driven Applications
Systems (DDDAS) paradigm, where data are dynamically integrated into an executing simulation to
augment or complement the application model, and, where conversely the executing simulation steers
the measurement (instrumentation and control) processes of the application system. In essence, the
InfoSymbiotics/DDDAS control loop unifies complex computational models of a system with the real-
time data-acquisition and control aspects of the system, and engenders transformative advances in
computational modeling of applications and in instrumentation and control systems, and in particular
those that represent dynamic, complex systems. Initial work on DDDAS has accomplished much
towards demonstrating its potential and broad impact. The concept is recognized as key to important
new capabilities, critical in many societal, commercial, and national and international priorities and
initiatives, identified in important studies, blue ribbon-panels and other notable reports. The 2005
NSF Blue Ribbon Panel on Simulation Based Engineering Science characterized DDDAS as visionary
and revolutionary concept. Recently published scientific and technological roadmaps such as the NSF
CyberInfrastructure Framework for the 21st Century (CIF21) and the Air Force Technology Horizons
2010 Report highlight the need for advances requiring the integration of simulation, observation and
actuation, as envisioned in the InfoSymbiotics/DDDAS concept. InfoSymbiotics/DDDAS has
transitioned from being a concept to becoming an area, one may say a new field, driving future
research and technology directions towards new capabilities. The present report outlines a research
agenda, integrating the multidisciplinary research scope of DDDAS with opportunities motivated by the
referenced roadmaps and recent technological advances, and transmits the research community’s call
for systematic support of such a research agenda.

A confluence of needs and recent technological advances render InfoSymbiotics/DDDAS approaches
more opportune than ever. Systems of today and those foreseen in the future, be they natural,
engineered or societal, will provide unprecedented opportunities for new capabilities, but with
concomitant increased scales of complexity and interconnectivity. The ensuing “systems of systems”,
exhibit increased fragility where even small failures in a subset of any of the component systems have
the potential of cascading effects across the entire set of systems. These new realities call for more
advanced methods of systems analysis and management. The methods needed go beyond the static
modeling and simulation methods of the past, to new methods, such as InfoSymbiotics/DDDAS which
augment and enhance the system models through continually updated information from monitoring
and control/feedback aspects of the system. Moreover, the need for autonomic capabilities and
optimized management of dynamic and heterogeneous resources in complex systems makes ever
more urgent the need for DDDAS approaches, not only at the design stage, but also for managing the
operational cycle of such systems. Together with these driving needs of emerging systems, several
technological and methodological advances over the last decade have produced added opportunities
and impetus for DDDAS approaches. These include: multi-scale/multi-modal modeling; ubiquitous
sensoring and networks of large collections of heterogeneous sensors and actuators; increased
networking capabilities for streaming large data volumes; multicore-based transformational
computational capabilities at the high-end, and the real-time data acquisition and control systems.

Capitalizing on the promise of the DDDAS concept and the successes of precedent initial research
efforts, a multi-agency workshop, cosponsored by AFOSR and NSF, was convened on August 30-31,
2010, in Arlington VA, and attended by over 100 representatives from academia, industry and
government, to address further opportunities that can be pursued and derived from
InfoSymbiotics/DDDAS approaches and advances, and in the context of the changed landscape of
underlying technologies and drivers referenced above. The scope of relevant efforts spans several
dimensions, and requires multidisciplinary thinking and multidisciplinary research, for innovations in
the entire hierarchy: from instrumentation for sensing and control, to the systems software, to the
algorithms, to the applications built using them. The report identifies needs in each of these areas as
well as critical science and technology challenges that must be met, and calls for synergistic research:
• in applications (for new methods where simulations are dynamically integrated with real-time data
acquisition and control, and where application models are dynamically invoked); • in algorithms
(tolerant in their stability properties to perturbations from streamed data, and algorithmic methods for
uncertainty quantification and for efficient estimation of error propagation across dynamically invoked
application models; • in systems software supporting applications that exhibit dynamic execution
requirements (where models of the application are dynamically invoked, and where the application

computational load, across the high-end platform and the sensors or controllers side, shifts across
these platforms, during execution-time, depending on the DDDAS application’s dynamic requirements,
and on resource availability); • in instrumentation systems and “big-data” management (dynamic,
adaptive, optimized management of instruments and heterogeneous collections of networks of sensors
and/or networked controllers); and • in cyberinfrastructures of unified computational and
instrumentation platforms and their environments. These, are not only opportunities for highly
innovative research advances, but also opportunities that can bridge academia and industry, inducing
new and innovative directions in industry and developing a globally competitive workforce.

InfoSymbiotics/DDDAS is a well-defined concept, and a well-defined research agenda has been
articulated through this and previous workshops and reports, and a multi-agency program solicitation
in 2005. Advances made thus far through InfoSymbiotics/DDDAS add to this promise, albeit they have
been achieved through limited and fragmented support. A diverse community of DDDAS researchers
has been established over the years, drawing from multiple disciplines, and spanning academe,
industry research and governmental laboratories, in the US and internationally. As was resoundingly
expressed in the August 2010 Workshop, these research communities are highly energized, by the
success thus far and by the wealth of ideas in confluence with other recent technological advances, all
of which provide added stimulus for increasing research and development efforts around the DDDAS
concept. All these, make timely a call for action for systematic support of research and technology
development, necessary to nurture furthering knowledge, and bring these advances to the levels of
maturity needed for enabling the transformative impact recognized as ensuing from
InfoSymbiotics/DDDAS.

ii

Report Outline

Executive Summary
1. Introduction - InfoSymbiotics/DDDAS Systems
2. InfoSymbioticSystems/DDDAS Multidisciplinary Research

3. Timeliness for Fostering InfoSymbiotics/DDDAS Research

3.1 Scale/Complexity of Natural, Engineered and Societal Systems
3.2 Applications’ Modeling and Algorithmic Advances
3.3 Ubiquitous Sensors
3.4 Transformational Computational and Networking Capabilities
4. InfoSymbiotics/DDDAS and National/International Challenges
5. Science and Technology Challenges discussed in the Workshop
5.1 Algorithms, Uncertainty Quantification, Multiscale Modeling
5.2 Large, Complex, and Streaming Data
5.3 Autonomic Runtime Support in InfoSymbiotics/DDDAS
5.4 InfoSymbioticSystems/DDDAS CyberInfrastructure Testbeds
5.5 InfoSymbioticSystems/DDDAS CyberInfrastructure Software Frameworks
6. Learning and Workforce Development
7. Multi-Sector, Multi-Agency Co-operation
8. Summary

Appendices
Appendix-0 Workshop Agenda
Appendix-1 Plenary Speakers Bios
Appendix-2 List of Registered Participants
Appendix-3 Working Groups Charges

1. Introduction - InfoSymbiotics/DDDAS Systems

InfoSymbioticSystems1/DDDAS embody the power of the Dynamic Data Driven
Applications Systems (DDDAS) paradigm, where data are dynamically
integrated into an executing simulation to augment or complement the
application model, and, where conversely the executing simulation steers the
measurement (instrumentation and control) processes of the application
is
a
system. In essence, the DDDAS control loop unifies complex computational
paradigm
in
which
on-‐line
or
models of an application system with the real-time data-acquisition and control
archival
data
are
used
for
aspects of the application system. The core ideas of the vision engendered by
updating
an
executing
the DDDAS concept have been well articulated and illustrated in two previous
simulation
and,
conversely,
NSF Workshop Reports in 2000 and in 2006[1,2] as well as presentations and
the
simulation
steers
the
papers of research projects in a series of International DDDAS Workshops
instrumentation
process…
inaugurated in 2003 [3], and a 2005 multi-agency Program [4]. Initial work
…
integration/unification
of
on DDDAS has accomplished much towards demonstrating the potential and
application
simulation
models
broad impact of the DDDAS paradigm. A confluence of several technological
with
the
real-‐time
data-‐ and methodological advances in the last decade has produced added
acquisition
and
control
opportunities and impetus for integrating simulation with observation and
actuation as envisioned in InfoSymbiotics/DDDAS, in ways that can transform
many more areas where information systems touch-on, be it natural,
engineered, societal, or other environments. Such advances include: the
increasing emphasis in complex systems multi-scale/multi-modal modeling and
algorithmic methods; the recent emphasis and advances towards ubiquitous
sensoring and networks of large collections of heterogeneous sensors and
actuators; the increase in networking capabilities for streaming large data
volumes remotely; and the emerging multicore-based transformational
computational capabilities at the high-end and the real-time data acquisition
and control systems. All this changing landscape of underlying technologies
makes it more than ever timely the impetus to increase research efforts
around the InfoSymbiotics/DDDAS concept.

Starting with the NSF 2000 DDDAS Workshop and the resulting report [1],
research efforts for enabling the DDDAS vision have commenced under
governmental support, in the beginning as seeding-level projects, and later
with a larger set of projects initiated through the 2005 multi-agency Program
Solicitation. Under this initial support, research advances have been made,
together with the increasing recognition of the power of the DDDAS concept.
The 2005 NSF Blue Ribbon Panel [5] characterized DDDAS as visionary and
…
revolutionary concept. A Workshop on DDDAS convened in 2006 produced a
…visionary
and
revolutionary
report [2] which in its comprehensive scope covers scientific and technical
concept

advances needed to enable DDDAS capabilities, presents progress made

–
Prof.
Tinsley
Oden
towards addressing such challenges, and provides a wealth of examples of

application areas where DDDAS has started making impact. Building upon a
…
DDDAS
key
for
objectives
in
2007 CF21 NSF Report [6] and several more recent TaskForces [7], the
Technology
Horizons

recently enunciated vision of the National Science Foundation

–
Prof.
Werner
Dahm
CyberInfrastructure for the 21st Century (CIF21 - NSF 2010)[8], lays out “a

(former
AF
Chief
Scientist)
revolutionary new approach to scientific discovery in which advanced

computational facilities (e.g., data systems, computing hardware, high speed
networks) and instruments (e.g., telescopes, sensor networks, sequencers) are
coupled to the development of quantifiable models, algorithms, software and
other tools and services to provide unique insights into complex problems in
science and engineering.” The InfoSymbiotics/DDDAS paradigm is well aligned
with and enhances the CIF21 vision. The more recent TaskForces ([7]), set-up

1
The
term
InfoSymbiotics
or
InfoSymbioticSystems
is
meant
to
be
tautonymous
to
DDDAS
and
was
introduced
in
recent
years
by
F.
Darema,

as
an
alternative
to
the
more
mathematical
term
Dynamic
Data
Driven
Applications
Systems
(DDDAS)
she
introduced
in
2000.
To
be
noted

though
that
“DDDAS”
has
become
“part
of
the
vernacular”
and
we
will
continue
to
use
it
in
this
report,
interchangeably,
or
together
with
the

terms
InfoSymbiotics
and
InfoSymbioticSystems
(as
or
InfoSymbioticSystems/DDDAS).

DDDAS
is
said
to
be
creating
a

new
field
of
unification
of
traditionally
distinct
aspects
of
an
application,
namely
unification
of
the
application
computational
model
(or

simulation)
with
the
measurement-‐data
(instrumentation
&
control)
components
of
the
application
system,
that
is:
“Unification
of
the
High-‐
End
Computing
with
the
Real-‐Time
Data-‐Acquisition
and
Control”;
the
term
InfoSymbiotics
is
used
to
denote
this
new
field.

by NSF, have reported-back with recommendations reinforcing the need for
this thrust, as do “14 Grand Challenges” posed by the National Academies of
Engineering [9]. In a similar if more targeted and futuristic vision, the recent
Technology Horizons 2010 Report [10] developed under the leadership of Dr.
Werner Dahm, as Chief Scientist of the Air Force, declares that “Highly
adaptable, autonomous systems that can make intelligent decisions about their
battle space capabilities … making greater use of autonomous systems,
reasoning and processes ...developing new ways of letting systems learn about
their situations to decide how they can adapt to best meet the operator's
intent” are among the technologies that will transform the Air Force in the next
20 years. Dr. Dahm has specifically called-out DDDAS as key concept in many
of the objectives set in Technology Horizons. Thus, InfoSymbotics/DDDAS has
transitioned from being a concept, to becoming an area, one may say a new
field, driving future research and technology directions towards new
capabilities. Therefore more than ever, it’s now timely to increase the
emphasis for research in the fundamentals and in technology development to
create a well-developed body of knowledge around InfoSymbiotics/DDDAS as
well as the ensuing new capabilities.
Thus,
InfoSymbotics/DDDAS

has
transitioned
from
being
a
Capitalizing on the promise of the DDDAS concept and the precedent initial
concept
to
becoming
an
area,
research efforts, on August 30-31, 2010, a multi-agency Workshop was
one
may
say
a
new
field,
convened to address further opportunities that can be pursued and derived
driving
future
research
and
from InfoSymbiotics/DDDASs approaches and advances. The Workshop, co-
technology
directions
towards
sponsored by the Air Force Office of Scientific Research and the National
new
capabilities.

Science Foundation, was attended by over 100 representatives from academia,
government and industry, and explored these issues. The Workshop opened
with remarks by Dr. Werner Dahm, and Senior Leadership of the co-sponsoring
agencies[14]. The remainder of the Workshop was organized into Plenary
Presentations, Working Group Sessions, and out-briefs of the Working Groups
[Appendix 0 – Agenda]. The plenary keynote presentations [14], by a
distinguished set of speakers [Appendix1- Bios of Keynotes], addressed several
key application areas, discussed the need and the impact of new capabilities
enabled through DDDAS, and showcased progress that has been made in
advancing fundamental knowledge and technologies contributing towards
enabling DDDAS capabilities in important application areas. Prior to the
workshop, a number of questions had been developed by the workshop co-
chairs together with the working groups co-chairs and participating agencies
program officials, and posed to the attendees [Appendix-2 – List of
Participants; and Appendix-3 - WG Charges]. The working groups addressed
these questions, as well as other topics brought-up during break-out and
plenary discussions.

The main science and technology challenges discussed in the August2010
Workshop have also been well articulated in previous DDDAS workshops and
their reports [1,2], as well as the 2005 DDDAS program solicitation. Therefore
in the present report, discussions whose conclusions and recommendations
have already been presented in the previous two reports are referred to
synoptically here and the reader is referred to these previous documents for
further details. The present report, in summarizing deliberations of the recent
workshop, focuses on selected topics that are complementary to and add
substantially to the previous reports in the context of recent advances and
drivers. The 2nd Chapter of the present report provides a synopsis of the broad
science and technology components that have also been identified in the
previous reports and in the solicitation. In the 3rd Chapter are addressed key
elements of new drivers and new opportunities, together with challenges and
impacts in increasing synergistic research efforts on InfoSymbiotics/DDDAS.
Subsequent chapters and subsections are organized around questions posed to
the participants of the workshop, but addressing more specific issues related to
more recent research directions and new opportunities, as they relate to
InfoSymbiotics/DDDAS; specifically: a) applications modeling and algorithms,
such as multiscale modeling, dynamic data assimilation, uncertainty
quantification, b) ubiquitous data management, c) systems software for

2

seamlessly integrated high-end with data acquisition and control systems
taking advantage of emerging multicore processing directions, and d) the
wealth of new CyberInfrastructure projects serving as laboratories and
testbeds of a diverse set of science and engineering discovery efforts, and
where InfoSymbiotics/DDDAS can have transformative impact.

2. InfoSymbioticSystems/DDDAS Multidisciplinary Research

The InfoSymbioticSystems/DDDAS paradigm engenders transformative
advances in computational modeling of applications and in instrumentation and
control systems (and in particular those that represent dynamic systems).
Enabling DDDAS capabilities involves advances, through individual research
efforts but mostly through synergistic multidisciplinary research, in four key
science and technology frontiers: applications modeling, mathematical and
statistical algorithms, systems software, and measurement (instrumentation
and control) systems. Multidisciplinary thinking and multidisciplinary research
are imperative, and specifically through synergistic and systematic
collaborations among researchers in application domains, in mathematics and
statistics, in computer sciences, as well as those involved in the
design/implementation of measurement and control systems (instruments and
instrumentation methods, and other sensors and embedded controllers).
Cyberinfrastructure is the collective set of technologies spanning “computing
systems, data, information sources, networking, digitally enabled-sensors,
instruments, virtual organizations, and observatories, along with interoperable
suite of software services and tools” [6]. InfoSymbiotics/DDDAS environments
push these sets of technologies and their collective interoperability to new
levels, and require architectural software frameworks, comprehensively
representing the corresponding cyberinfrastructures, and at the same time
require advanced testbeds that will allow experimentation on the new
capabilities sought.

Challenges and opportunities for individual and multidisciplinary research,
requires
synergistic,
technology development, and software-hardware frameworks requisite for
multidisciplinary
thinking
and
InfoSymbioticSystems/DDDAS-related Cyberinfrastructures are summarized
multidisciplinary
research
below, along these four key frontiers:
along
four
science
and
• Applications modeling: In InfoSymbiotics/DDDAS implementations, an
technology
frontiers:

application/simulation must be able to accept data at execution time and
applications
modeling,
be dynamically steered by such dynamic data inputs. The approach results
into more accurate modeling and ability to speed-up the simulation (by
algorithms,
computer
and

augmenting and complementing the application model or replacing
information
sciences,
and

targeted parts of the computation by the measurement data), thus
instrumentation
systems

improving analysis and prediction capabilities of the application model and
yielding decision support systems with the accuracy of full-scale
simulations; in addition, application-driven instrumentation capabilities in
DDDAS enable more efficient and effective instrumentation and control
processes. This requires research advances in application models,
including: methods that allow incorporating dynamic data inputs into the
models, and augmenting and/or complementing computed data with actual
data, or replacing parts of the computation with actual data in selected
regions of the phase-space of the problem; models describing the
application system at different levels of detail and modalities (multi-
scale/multi-level, multi-modal modeling) and ability to dynamically invoke
appropriate models as induced by data dynamically injected into the
executing application; and interfaces of applications to measurements and
other instrumentation systems. A key point is that DDDAS leads to an
integration of (large-scale) simulation modeling with traditional controls
systems methods, thus providing impetus for new directions to traditional
controls approaches.
• Mathematical and Statistical Algorithms: InfoSymbiotics/DDDAS require
algorithms with stable and robust convergence properties under
perturbations induced by dynamic data inputs: algorithmic stability under

3

dynamic data injection/streaming; algorithmic tolerance to data
perturbations; multiple scales and model reduction; enhanced
asynchronous algorithms with stable convergence properties; uncertainty
quantification and uncertainty propagation in the presence of multiscale,
multimodal modeling, and in particular in cases where the multiple scales
of models are invoked dynamically, and there is thus need for fast
methods of uncertainty quantification and uncertainty propagation across
these dynamically invoked models. Such aspects push to new levels the
traditional challenges of computational mathematical and statistical
approaches.
• Application Measurement Systems and Methods InfoSymbiotics require
innovations in the entire hierarchy of instrumentation for sensing and data
acquisition and control, systems software, algorithms and applications built
using them. In each of these areas there are critical science and
technology challenges that must be met, improvements in the means and
methods for collecting data, focusing in a region of relevant
measurements, controlling sampling rates, multiplexing, multisource
information fusion, and determining the interconnectivity and overall
Enables
decision
support
architecture of these systems of heterogeneous and distributed sensor
systems
with
accuracy
of
full
networks and/or networks of embedded controllers. Advances through
scale
simulations…
InfoSymbioticSystems/DDDAS will create improvements and innovations in
…
creates
powerful
methods
instrumentation platforms, and will create new instrumentation and control
for
dynamic
and
adaptive
systems capabilities, including powerful methods for dynamic and adaptive
utilization
of
resource
utilization of resource monitoring and control, such as collections of large
monitoring
and
control,such
number of heterogeneous (networked) sets of sensors and/or controllers.
• Advances in Systems Software runtime support and infrastructures, to
as
collections
of
large
number

support the execution of applications whose computational resource
of
heterogeneous
(networked)

requirements are adaptively dependent on dynamically changing data
sets
of
sensors
and/or

inputs, and include: adaptive mapping (and re-mapping) of applications
controllers…
(as their requirements and underlying resources change at execution time)
through new capabilities, such as compiler-embedded-in-the-runtime
(runtime-compiler) approaches; dynamic selection at runtime of
application components embodying algorithms suitable for the kinds of
solution approaches depending on the streamed data, and depending on
the underlying resources, dynamic workflow driven systems, coupling
domain specific workflow for interoperation with computational software,
general execution workflow, and software engineering techniques.
Software Infrastructures and other systems software (OS, data-
management systems and other middleware) services to address the “real
time” coupling of data and computations across a wide area heterogeneous
dynamic resources and associated adaptations while ensuring application
correctness and consistency, and satisfying time and policy constraints.
Other capabilities needed include the ability to process large-volumes and
high-rate data from different sources including sensor systems, archives,
other computations, instruments, etc.; interfaces to physical devices
(including sensor systems and actuators), and dynamic data management
requirements. The systems software environments required are those that
can support execution in dynamically integrated platforms ranging from
the high-end to the real-time data acquisition and control - cross-systems
integrated.

Research that has been conducted thus far has already started making
progress in addressing a number of these challenges and across multiple
domains as discussed above. Not only there is progress to be made along the
traditional S-curve in each of these domains, but in essence there is need for
coordinated progress across multiple S-curves. That is challenging indeed.
However, based on fundamental knowledge advances made thus far, the
recognition of the transformative capabilities of the DDDAS concept, and other
emerging methodological and technological advances (discussed next), all
these are fueling the interest to increase efforts and systematic support for
InfoSymbiotics/DDDAS.

4

3. Timeliness for Fostering InfoSymbiotics/DDDAS Research

3.1 Scale/Complexity of Natural, Engineered and Societal Systems

Today not only such systems are becoming increasingly more complex, but
also we deal with environments which involve “systems-of-systems”, of
multiple combined engineered systems, of engineered systems interacting with
natural systems, engineered systems with humans-in-the-loop; such as for
example are: all types of complex platforms, communication systems, wide-
area manufacturing systems, large national infrastructure systems (“smart
grids”, such as electric power delivery systems), and threat and defense
systems.

systems
are
becoming
The increase in both complexity and degree of interconnectivity in such
increasingly
more
complex…
systems, provides unprecedented opportunities for new capabilities, and at the

same time drives the need for more advanced methods for understanding,
…“systems-‐of-‐systems”…

building, and managing such systems in autonomic ways. Furthermore, this

complexity has added to the fragility of such systems. As the interconnectivity
…today’s
complex
systems
are
across multiple systems has increased tremendously, so has the impact of
in
cascading effects across the entire set of systems, for even small failures in a
nature

subset of any of the component systems.

These new realities have led to the need for more adaptive analysis of
systems, with methods that go beyond the static modeling and simulation
methods of the past, to new methods, such as InfoSymbiotics/DDDAS, which
augment and enhance the system models through continually updated
information from monitoring and control/feedback aspects of the system.
Moreover, the need for capabilities of optimized management of dynamic and
heterogeneous resources in complex systems makes ever more urgent the
need for DDDAS approaches, not only at the design stage, but also for
managing the operational cycle of such systems.

This report takes the thesis that most of today’s complex systems are
InfoSymbiotics/DDDAS in nature. Preliminary efforts in DDDAS, such as those
spawned through the comprehensive multiagency DDDAS Program Solicitation
in 2005, created an impetus for advances in DDDAS techniques in several
application areas, including for example management and fault-tolerant electric
power-grids (this and many other examples cited in the earlier 2006 DDDAS
Report [2]). Moreover, there are many other systems that have complexity
and dynamicity in their state space, making the use of InfoSymbiotics/DDDAS
approaches not only essential but imperative.

3.2 Applications’ Modeling and Algorithmic Advances

A second factor that favors renewed and coordinated investment now, are the
rapid advances in a number of algorithmic methods relevant for creating
DDDAS capabilities. In fact, together with increased research emphasis in
multi-scale modeling and new algorithmic methods, for dynamic systems
DDDAS drives further the capabilities and the needs for new capabilities in
multimodal and multiscale modeling and algorithms, both numeric and non-
numeric, and where these multiple levels and modalities are invoked
dynamically. Such new advances that can be exploited in the context of DDDAS
include non-parametric statistics allowing inference for non-Gaussian systems,
faster methods for uncertainty quantification (UQ) and uncertainty
propagation, as well as advances in the numerics of stochastic differential
equations (SDEs), parallel forward/inverse/adjoint solvers, and smarter data
assimilation, (that is: dynamic assimilation of data with feed-back control to
the observation and actuation system), hybrid modeling systems and math-
based programming languages.

5

Simulations of a system are becoming synergistic partners with observation
and control (the “measurement” aspects of a system). It was highlighted at the
workshop that creating DDDAS brings together the application modeling and
simulation research communities with the controls communities, on synergistic
research efforts to create these new applications and their environments,
where the (often high-end) simulations modeling is dynamically integrated with
the real-time data acquisition and control components of the application.
Workshop participants, representatives of the controls communities (and
several of them principal investigators of projects that have commenced under
the DDDAS rubric) highlighted the fact that DDDAS opens new domains for
controls research.

3.3 Ubiquitous Sensors

A third factor is the increasing ubiquity of sensors – low cost, distributed
intelligent sensors have become the norm in many systems and environments,
which include: terrestrial, airspace and outer-space, underwater and
underground. Major investments in satellites, manned and unmanned aerial
vehicles equipped with multitudes of sensors, and other observation systems
are now coming online; in the subsequent section a wealth of examples are
overviewed, drawn from the CIF21, the TechHorizons, the 2006 DDDAS
Reports, and other sources. Examples range from environmental observation
systems, to phone geo-location information and instruments in automobiles,
are already in place, collecting and/or transmitting data, even without the
ubiquitous,
heterogeneous
user’s knowledge or involvement.
collections
of
networked

sensors
and
controllers
…
Static or ad-hoc ways of managing such collection of data, in general produces
…
managing
…
in
static
and
very large volumes of data that need to be filtered, transferred to applications
ad-‐hoc
ways
inadequate…

that require the data, and possibly partially archived. Given the ubiquity of
allows
sensors and other instrumentation systems, and the ubiquity and data
adaptive,
optimized
volumes, tradeoffs need to made, for example between which data are
management
of
these
collected and bandwidth available. Architecting and managing large numbers
heterogeneous
resources…

and heterogeneous resources cannot be done in the ad-hoc and static ways

pursued thus far, it’s woefully inadequate. DDDAS provides a powerful
methodology for exploiting this opportunity of ubiquitous sensoring, through
the DDDAS-intrinsic notion of having the executing application-model control
and dynamically schedule and manage these heterogeneous resources of
sensors (and actuators or controllers).

In fact, in DDDAS, the application model becomes unified and seamlessly
integral with the real-time data-acquisition and control application-components
executing on the sensors and the actuators. Moreover, as is discussed later in
the systems software section, it becomes variable as to what parts of the
application model execute on the higher-end platforms, versus what may
execute at the sensor or the controller side, and such variability in execution
requirements depends for example on bandwidth and other resource
limitations, which will dictate that data preprocessing, compression, and
combining, may need to happen at the sensor or controller side. Thus, how to
architect these networks of sensors and sets controllers, and how to
dynamically schedule and utilize these heterogeneous resources is guided by
the executing simulation model, as this is a fundamental premise in DDDAS.

3.4 Transformational Computational and Networking Capabilities

A fourth factor is the dramatic transformation of the computing and networking
environment. The advent of multicore/manycore chips as well as
heterogeneous architectures like GPUs, create revolutionary advances in
heterogeneous distributed computing and in embedded computing. All these
new directions are leading to unparalleled levels of increased computing
capabilities and at reduced cost. In the midst of this transformation – many

6

technologies that were exclusively HPC (e.g. massively parallel processing) can
now used at all scales from high end machines to desktop and embedded
systems.

Recent years have seen a continuous movement towards embracing dynamic
data throughout all areas of computing. For example, as HPC applications look
towards exascale computing with billion-fold concurrency, applications will
need to be fundamentally aware of their environment and able to react
dynamically to self optimize or self heal. The emergence of Cloud computing is
elevating the demand and expectations for on-demand computing. As data
becomes pervasive across scientific disciplines supercomputer centers are
recognizing and embracing data intensive needs. Data availability is catalyzing
new computational disciplines across the arts and humanities. New mobile
platforms are leading to an explosion in web 2.0 and social networking
technologies which are intrinsically data driven and location aware. The time is
right to leverage the decade of experience in academia of grid computing and
distributed applications to steer a path that integrates agencies and industry to
support dynamic data driven applications.

At the same time, network bandwidths have also undergone transformative
advances – for example, the DOE/ESNET network expects to have the ability to
transfer 1TB in less than 8 hours [10], allowing transfer of large volumes of
data from distributed sources, such as remote instruments and other sensors,
and also ability of remote on-line control of networks of such sensors and
controllers. Commercial networks expect to provide 100Gbps in the near
future. Together with increasing bandwidth capabilities at the wired and
wireless domains, the ramping-up adoption of IPv6 increase opportunities of
…
MPUs
populating
the
range
exploiting further the ubiquitous interconnectivity, across multitudes of
of
computational
and
heterogeneous devices with increased transmission rates. Such capabilities
instrumentation
platforms
…
create interconnectivity across peta- and exa-scale capacity platforms,
from
the
high-‐end
to
the
real-‐ connecting them at the same time also to instrumentation systems (networks
of sensors and networks of controllers).
time
data
acquisition
and

control…

…
opportune
to
exploit
in
DDDAS entails unification across the large-scale computing
(simulations/modeling) and the real-time data-acquisition and control.

Commensurately, in DDDAS environments, the respective range of supporting

platforms are also becoming a unified platform, from the high-end to the
sensors and controllers. These new environments require significant advances
in systems software to support the dynamic integration and a highly
heterogeneous runtime across this range of platforms. While this is a
formidable software challenge, the advent of multicores is an aspect that
somewhat simplifies one dimension of this challenge, as it will be the same
kinds of multicores (MPUs – Multicore Processing Unit) populating the high-end
platforms as well as the instrumentation systems.

Thus, the increasing emergence of ubiquitous sensing, high bandwidth
networking, and the unprecedented levels and range of multicore-based
computing that are becoming available, all these are timely advances,
providing impetus for exploiting DDDAS to create new capabilities in numerous
critical applications and application areas.

4. InfoSymbiotics/DDDAS and National/International Challenges

In the 2006 DDDAS Report [2], over 60 projects are listed. It is discussed
there how these projects are advancing the capabilities, through DDDAS
approaches, and are impacting in a wide set of areas, all key and critical in the
civilian, commercial, and defense sectors. Specifically: in Physical, Chemical,
Biological, Engineering Systems (e.g.: chemical pollution transport -
atmosphere, aquatic, subsurface, in ecological systems, protein folding,

7

molecular bionetworks,…); in Critical Infrastructure Systems (e.g.:
communications systems, electric power generation and distribution systems,
water supply systems, transportation networks, and vehicles -air, ground,
underwater, space; monitoring conditions, prevention, mitigation of adverse
effects, recovery, …); in Environmental (e.g.: prevention, mitigation, and
response, for earthquakes, hurricanes, tornados, wildfires, floods, landslides,
tsunamis, terrorist attacks); in Manufacturing (e.g.: planning and control); in
Medical and Health Systems (e.g.: MRI imaging, cancer treatment, control of
brain seizures, …); in Homeland Security (e.g.: terrorist attacks, emergency
response); and, in a boot-strapping way, in Dynamic Adaptive Systems-
Software (e.g.: robust and dependable large-scale systems; large-scale
computational environments).

In addressing the future AirForce, the Technology Horizons Report has called
for revolutionary new directions in complex systems, that apply beyond the
domains of interest to the AirForce, to systems which have corresponding
analogues in the civilian, commercial sectors, for example in manufacturing
and environmental concerns. As articulated in Technology Horizons, these
dozen directions are:
• From (design of customized) Platforms ...To (design for) Capabilities;
From … Fixed (specialized purpose systems) ...To … Agile (adaptive);
From … Integrated ...To … Fractionated (to allow plug-and-play
adaptability - leverage and reuse); From … Preplanned ...To … Composable
(to meet evolving needs); From … Long System Life ...To … Faster Refresh
• From … Control ...To … Autonomy; From … Manned (operator-based)
...To … Remote-Piloted (remote human or autonomic); From … Single-
InfoSymbiotics/DDDAS…

key
Domain ...To … Cross-Domain (to account for interoperability – systems-
for
autonomic
systems,
of-systems)
collaborative/cooperative
• From … Sensor (data collection) ...To … Information (dynamically
control…
ad-‐hoc
networks…
processed data); From … Permissive (passive) ...To … Contested (active
sensor
based
processing…
and changing situations)
agile,
composable,
…

• From … Cyber Defense (fire walls) ...To … Cyber Resilience (fault-

…cyber
resilient…

tolerance, recovery); From … Strike (corrective action) ...To …

Dissuasion/Deterrence (prevention, mitigation).
To address such challenges, a number of core scientific and technological

capabilities are also emphasized in the TechHorizons Report. The identified

key areas are: autonomous systems; autonomous reasoning and learning;
resilient autonomy; complex adaptive systems; V&V for complex adaptive
systems; collaborative/cooperative control; autonomous mission planning; ad-
hoc networks; polymorphic networks; agile networks; multi-scale simulation
technologies; coupled multi-physics simulations; embedded diagnostics;
decision support tools; automated software generation; sensor-based
processing; behavior prediction and anticipation; cognitive modeling; cognitive
performance augmentation; human-machine interfaces. As articulated by Dr.
Dahm in his remarks at the 2010 Workshop, InfoSymbioticSystems/DDDAS
play a major role in all these.

The 2007 NSF CF21 Report [6] provides a wealth of new science and
engineering research frontiers and their corresponding cyberinfrastrure
collaboratories. Nearly sixty examples of such major projects are listed in that
report (cf. pp.50-55 CIF21 ibid). The projects are exploring phenomena and
systems ranging across many dimensions, scales and domains of natural,
human, and engineered systems, and interactions there-of: from the nanoscale
to the terascale; from subatomic and molecular to the black-hole dynamics of
galaxies; from understanding the dynamics of the inner mantle of the earth, to
the atmospheric events, weather, climate, to outer space weather; from the
molecular levels of biological systems, the proteomics and genomics, to the
organismal, ecological and environmental systems; from the interplay among
genes, microbes, microbial communities to interactions with earth and space
systems - from the bio-sphere to the oceans, atmosphere, cryo-sphere; from
individualized models of humans to behaviors and complex social networks.

8

InfoSymbioticSystems/DDDAS are relevant to a multitude of the projects cited
in the CIF21 Report.

Keynote presentations at the August2010 DDDAS Workshop (some by principal
investigators in cyberinfrastructure projects referenced in CIF21) spoke about
the major role InfoSymbioticSystems/DDDAS plays in such efforts.
InfoSymbiotics/DDDAS-based advancements in several application areas,
environmental, civil-infrastructures, and health, were highlighted by the
speakers, and specifically: in weather forecasting and advancing the modeling
methods for climate analysis; in environmental monitoring and in water
management; in resource management in urban environments, in health
monitoring of complex airborne platforms; in the medical and pharmaceutical
application areas, where examples range from medical diagnosis and
intervention, like cancer treatment and advanced surgical procedure
capabilities, to genomics and proteomics, and customized drug delivery and
release in the human-body.

The 14 NAE Grand Challenges cover a range of important topics: Make solar
energy economical; Provide energy from fusion; Develop carbon
sequestration methods; Manage the nitrogen cycle; Provide access to clean
water; Restore and improve urban infrastructure; Prevent nuclear terror;
Engineer better medicines; Advance health informatics; Reverse-engineer the
brain; Enhance virtual reality; Secure cyberspace; Advance personalized
learning; Engineer the tools of scientific discovery.

While all these challenges and the related research directions and drivers have
been called by in principle different stakeholders and communities, it should be
…research
is
needed
at
the
noted that there is a remarkable affinity in the basic research areas and
fundamental
levels
and
in
advances needed spanning across the NAE Challenges, and those that are
technologies,
articulated in the TechHorizons, in CIF21, in the invited keynotes and other
…
in
order
to
climb
along
the
applications discussed at the August2010 DDDAS Workshop, and related
steep
part
of
multiple
DDDAS reports. Many other examples were discussed during the Working
innovation
S-‐curves,
and
Groups discussions. A sample is provided here:
create
robust


a) The role of DDDAS in energy: from designing and operating smarter power
capabilities
and
concomitant

plants and other power generation sources, like solar, water, and wind
CyberInfratructures

renewable energy power sources; effectively managing the power

distribution from such sources, and addressing current and foreseen

problems with the power grid, such as optimized management of
resources, addressing the powergrid fragility by predicting the onset of
failures, and stemming-off and mitigating cascading failures and limiting
their impact (see also 2006 DDDAS Report on existing energy-related
DDDAS projects with respect to that [2]).
b) In national security and defense applications, DDDAS can play a major role
in homeland security and in addressing decision-making, in battlefield
settings. For example, actual battlefields are cluttered with dense numbers
of fixed and moving objects, myriads of many types of sensors (radar,
EO/IR, acoustic, ELINT, HUMINT etc.), and all their data need to be fused
in real-time. This deluge of data includes video-data which need to be
correlated with radar, SIGNIT, HUMINT and other non-optical data. Lt.
Gen. Deptula stated “(we are) swimming in sensors and drowning in data’’.
Moreover, these data are incomplete, include errors, and need to be
optimally processed to produce unambiguous and target state vectors,
including time-dependent effects.
c) With respect to specific examples in civilian critical infrastructure
environments, already DDDAS projects included in the 2006 DDDAS Report
have covered incidents like: the 2005 Hurricane Katrina event in New
Orleans [2], and using DDDAS to monitor and predict the propagation of
oil spills (e.g. Douglas et al [2], Patrikalakis et al [2]). The recent Macondo
oil spill in the Gulf of Mexico showed the need for better predictions of the
spread of the oil in order to take more effective mitigating actions.
Moreover, in the aftermath of this disastrous event, the problem of

9

determining the residual oil and its locations, called for the advancing the
kind of work that had started through some small DDDAS efforts on oil-
spill propagation. Observations involve tracking the residual oil from a
large set of heterogeneous sources of data, such from satellites and visual
inspection, ocean water sampling, tracking winds and oceanic currents,
and air and water temperature measurements, all of which are in nature
multimodal, dynamic and involve multiple scales, requiring fusion of such
heterogeneous sets of data. Moreover all these data need to be integrated
on-line with coupled models of wind and oceanic circulation models in a
DDDAS-loop. These are research endeavors which the above referenced
research projects started to pursue. However, further research is needed
at the fundamental levels and in technologies, in order to climb along the
steep part of multiple innovation S-curves, and to create the robust
DDDAS cyberinfrastructure frameworks that would allow such analysis to
be done at the scale needed for a Macondo-like event.

5. Science and Technology Challenges discussed in the Workshop

Successful DDDAS require innovations in the entire hierarchy: from
instrumentation for sensing and control, to the systems software, to the
algorithms and application models, and the cyberinfrastructure software
frameworks encompassing this integrated hierarchy. In each of these areas
there are critical science and technology challenges that must be met.

The 2006 DDDAS Report [2] discusses advances and open issues in
applications modeling, algorithms, runtime systems discussed in that report

include the need to go beyond traditional approaches to develop application
entails
invoking
multiple
models able to interface and be steered by real-time data streamed into the
modalities
and
model
scales…
model, dynamic model selection, and multiscale and multimodal modeling
dynamically
at
run-‐time…

methods where the multiple scales and modalities of models are dynamically
driven
by
dynamic
data
inputs
invoked based on the streamed data for dynamic-data driven, on-demand
…
observability,
identifiability,
scaling and resolution capabilities; uncertainly quantification and uncertainty
tractability
and
dynamic
and
propagation across dynamically invoked models; self-organization of
continuous
validation
and
measurement systems, application workflows; observability, identifiability,
verification
(V&V)
tractability and dynamic and continuous validation and verification (V&V) of

models, algorithms, and systems. That report also includes discussions on

methods related to computational model feedback on the instrumentation data
and control system relevant advances such as instrumentation control, data
relevance assessment, noise quantification and qualification, and robust
dynamic optimization, sensor and actuator steering. In mathematical and
statistical algorithms the 2006 Report discusses progress and open issues on
methods related to the measurement/data-feedback on the computational
model that requires algorithms tolerant and stable under perturbations from
streamed data, dynamic data assimilation methods, stochastic estimation for
incomplete, possibly out of time-order data, and fast error estimation. In the
area of systems software the 2006 DDDAS Report provides an extensive
discussion on the requirements and systems services for dynamic adaptive
runtime and end-to-end support for DDDAS environments, and namely support
the applications’ data- and knowledge-driven-, and adaptive composition and
time- constrained execution and feedback-control with instrumentation
systems.

These research and technology challenges and the need for systematic support
and concerted multidisciplinary research efforts were also overviewed during
the present workshop, and the participants reaffirmed the content and the
recommendations of the 2006 DDDAS Report. In the sections following here,
are discussed additional opportunities, identified by the participants of the
present workshop, and salient points are made in the context of emerging
underlying methodological and technological drivers and advances in modeling
and sensoring, and in computational and networking capabilities, as well as
cyberinfrastructure collaboratories and other testbeds recently available.

10

5.1 Algorithms, Uncertainty Quantification, Multiscale Modeling

DDDAS environments, where new data are streamed into the computation at
execution time and where application models can be invoked dynamically,
stress further the traditional requirements in terms of quantification of error in
data and uncertainty propagation not only within a model but across models
invoked dynamically at execution time. In DDDAS environments one is tasked
with uncertainty fusion of both simulation and observational data in dynamic
systems, design of low-dimensional and/or reduced order models for online
computing, decision-making and model selection under dynamic uncertainty.
In the broader context of UQ, one of the persistent challenges is the issue of
long-term integration. This refers to the fact that stochastic simulations over
long-term may produce results with large variations that require finer
resolution and produce larger error bounds. Though none of the existing
techniques is able to address the issue in a general manner, it is possible to
apply the DDDAS concept of augmenting the model through on-line additional
data injected into targeted aspects of the phase-space of the model, in-order
to reduce the solution uncertainty by utilizing selected measurement data.

To address these challenges, in the DDDAS context, new ideas need to be
explored, that either take advantage or further advances in existing methods in
UQ and multi-scale modeling. Notable approaches include: generalized
polynomial chaos methodology for UQ, Bayesian analysis for statistical

inference and parameter estimation (particularly to develop efficient sampling
…
requires
new,
faster
methods as standard Markov-Chain Monte-Carlo (MCMC) does not work in real
time), filtering methods (ensemble Kalman filter, particle filter, etc) for data
methods
for
uncertainty

assimilation, equation-free, multi-scale finite element methods, scale-bridging
quantification
&
propagation

methods for multi-scale modeling, sensitivity analysis for reduction of the
across
multiple
scales
of

complexity of stochastic systems, etc. These methods have been attempted
dynamically
invoked
models...

but their capabilities need to either be extended to the DDDAS domain, or new

methods and tools for UQ and multi-scale modeling be developed which can

satisfy the stringent dynamic DDDAS requirements. For example, methods for
adaptive control of complex stochastic and multi-scale systems, efficient means
to predict rare events and maximize model fidelity, methods for resource
allocation in dynamic settings, tools to reduce uncertainty (if possible) and
mitigate its impact.

Key challenges emerge in DDDAS for integrating the loop from measurements
to predictions and feedback for highly complex applications with incomplete
and possibly low quality data. Novel assimilation for categorical/uncertain data
(graphical models, SVMs) must be developed. These advances can be coupled
to recent advances in large-scale computational statistics and high-dimensional
signal analysis to enabling tackling of complex uncertainty estimation
problems. Algorithms for model analysis and selection (model error, model
verification and validation, model reduction for highly-nonlinear forward
problems, data-driven models) still need further research to create application
independent formulations. New algorithms in large-scale kernel density
estimation algorithms, on reduced order models and model reduction,
interpolation on high-dimensional manifolds, multiscale interpolation, and
manifold discovery for large sample sizes are examples of major breakthroughs
in the last decade that can be furthered in the context of
InfoSymbiotics/DDDASs to create the new levels of capabilities enabled
through the DDDAS concept.

In another dimension, all these new algorithmic approaches need to be
addressed and exploit the new computational platform architectures,
multicore-based MPUs and specialized accelerators like GP-GPUs, populating
embedded sensors and controllers, and Peta- and Exascale-scale HPC
platforms, Grids and Clouds, etc. These environments on one hand translate to
a need for distributed/parallel, fault-tolerant resource aware/resource adaptive
versions of the referenced algorithms, and on the other hand provide new

11

opportunities for powerful analysis schemes based on algorithmic approaches
as discussed above.

5.2 Large, Complex, and Streaming Data

Key advances over the recent years in data management but also increasing
challenges from the “data deluge” make evermore timely the role of the
DDDAS paradigm, both in creating InfoSymbiotics/DDDAS capabilities and at
the same time pushing the present advances in data management to new
dimensions for more effective and efficient management processes. There have
been impressive recent advances in commercial and academic support
infrastructures for what is often termed the “Big Data” problem [13]. Efficient,
scalable, robust, general-purpose infrastructure for DDDAS will addresses the
Big Data problem in the context of the “Dynamic Data” entailed in the DDDAS
paradigm – characterized by either (i) spatial-temporal specific information, (ii)
varying distribution, or (iii) data that can be changed in dynamic ways, e.g.,
either operated upon in-transit or by the destination of data so that the load
can be changed for advanced scheduling purposes. While Grids and Clouds
have attempted to address the core connectivity and remote data-access
issues between producers and consumers of data, DDDAS creates new levels of
requirements and engenders new levels of capabilities not addressed or
present in current state-of-the-art. The interoperability capabilities fostered
within the Grids model are important for DDDAS environments. On the other
hand, it’s an open question how the ubiquitous and remote streaming data
“Big
Data”
problem…

capabilities encountered in DDDAS environments can be accommodated within
“we
are
swimming
in
sensors
the Cloud model, which thus far is not addressing ubiquitous interoperability
and
drowning
is
data”…

-‐ Lt.
Gen
Deptula
One consequence of the “Big Data” problem is a need for reducing the data set

to that which is essential and carries the most information. DDDAS has the
concept
is
key
to
the
ability
for
intrinsic ability to help prioritize data collection to the most critical areas as
collecting
data
in
targeted
opposed to indiscriminate uniform acquisition, thereby greatly reducing the
ways
rather
than
volume of data needed to support prediction/decision making. In DDDAS, the
ubiquitously

executing application model guides what data are really needed to improve the

application analysis; this notion is key to collecting data in targeted ways

rather than ubiquitously. Also, in DDDAS, parts of the computation can be
replaced by the actual data, thus reducing the scaling of the computational
cost of the simulation model, but at the same time such methods also pose
unique requirements in managing data acquisition and ensuring QoS. Likewise,
DDDAS methods can also render more accurate reduced order modeling
methods, by using the actual and targeted data to construct the manifold of
extrapolation or interpolation among full-scale solutions, thus creating decision
support systems having the accuracy of full-scale models for many critical
applications. To enable these capabilities, formal methodologies need to be
developed, and software specifications as to what data is important and what is
important in data (i.e., data pattern recognition through templates or some
other system) and what to do when something important is found along with a
measure of uncertainty, thus reducing redundancy and describing data by
reduced order representations (e.g. features) instead of quantity, aspects that
are essential.

Typical algorithms today deal with persistent data, but not streaming data.
New algorithms and software are needed for supporting streaming data in the
DDDAS context, allowing on-the-fly, situation-driven decisions about what data
are needed at a given time and to reconfigure the data collection from sensors
in real-time, to push or pull-in more useful data. Rather than just “pull-in
more data ubiquitously”, as are the present static and ad-hoc approaches of
today, in DDDAS scheduling and correlating scheduling of multiple
heterogeneous sets of sensors is determined dynamically by the executing
application. Data collection, and scheduling data collection resources, is done
adaptively, and aspects like granularity, modality, and field of view, all these
are selectively targeted. Searching and discovering sensors and data must be

12

expressed through some functional representation, both algorithmically and by
software. Data collection and scheduling data collection resources is done
adaptively, and aspects like granularity, modality, and field of view, all these
are selectively targeted.

New strategies are needed for sensor, computing, and network scheduling.
Scheduling maybe be quasi-optimal, intelligent and automatic, to support the
needs of DDDAS environment. Need to evaluate which data and results are
critical for the executing application and which are not, and prioritize which
data to collect: “when and how”, “now or later”. Where, when, and how to do
the processing must be decided on-the-fly, so that data can be delivered and
reconfigured, models changed, and symbiotically make the DDDAS work.
Where and how include locally, centrally, (geographically) distributed through
networks, or some combination thereof. Methods and new tools are needed to
disambiguate semantic non-orthogonality in data and models (time, space,
resolution, fidelity, science, etc.). The gap needs to be bridged, between the
differential rates of innovation in data capture, computation, bandwidth, and
hardware.

Methods for fusing data from multiple sensors and models dynamically will
have to be developed that are on demand, context dependent, actionable, and
fast. Likewise, data mining in DDDAS requires similar advances, and must be
addressed in the dynamic and adaptive ways that are intrinsic to DDDAS. Data
security and privacy issues frequently arise in the data collection and must be
addressed in the DDDAS context where the data are acquired and streamed
into the models dynamically, therefore assessment of their integrity and
provenance must be done in real-time, and new mechanisms are needed to
support such capabilities. Smart data collection means faster results that are
useful. Such methods are expected to be general and applicable across a
range of applications and thus it is expected that multiple stakeholders would
require
benefit from the ability to detect content on-the-fly and to couple sensor data
seamlessly
integrated
runtime
with domain knowledge.
support
for
environments

spanning
from
the
high-‐end
to

the
real-‐time
data-‐acquisition
5.3 Autonomic Runtime Support in InfoSymbiotics/DDDAS
and
control…
support
dynamic

mapping
of
the
application
In DDDAS, the application environments consist of simulation models which
across
this
range
and
under
are dynamically integrated with the instrumentation components of the
dynamic
computational
and
application. These application components execute on a collection of platforms
which may span a wide range: from the high-end, mid-range, workstation-level
other
resource

and the hand-held, to measurement systems, instruments, sensors and
requirements…

actuators or embedded controllers (and networks thereof). That is, DDDAS

environments entail dynamic integration of the traditionally distinct application

simulation and real-time control domains, and where the application platform
becomes the unified collection of computational and instrumentation platforms
for the diverse range referenced above. Consequently the application program
will likely encompass heterogeneity of programming models commensurate to
the computational and real-time application components, and likely have
requirements that are typically changing during execution time.

Thus, DDDAS imply application execution requirements that are highly
dynamic. Not only the computational requirements of the simulation may
change at execution time depending on the data streamed into the simulation
model, but also at execution time additional models of the application maybe
invoked, depending on dynamic data inputs. Such changing computation-
needs require dynamic discovery of resources, and dynamic mapping (and
remapping, as needed) of the application/simulation program on these
resources. Moreover, depending on the rates of the streamed data and
depending on network resources availability, it may be that the parts of the
application executing at the instrumentation (sensors or controllers) side, they
may also change. For example, if there are limitations in bandwidth to
transmit all data collected from a sensor (or sensors) then some of the data

13

preprocessing and analysis may be performed at the sensors side, or
combining operations may be performed across a group of sensors, etc.

In the context of DDDAS, systems software involves specification languages,
programming abstractions and environments, software platforms, and
execution environments, including runtimes that stitch together dynamically
reconfigurable applications. Given the vast diversity of DDDAS application
areas, platforms of interest encompass the range from distributed and parallel
systems to mobile and/or energy efficient platforms that assimilate sensors
inputs. Core DDDAS components by definition have evolved from executing on
static platforms with fixed inputs to executing on heterogeneous platforms with
widely varying capabilities fed by real-time sensing. Algorithms and platforms
must evolve symbiotically to effectively utilize each other’s capabilities.
Algorithmically, we need to develop along three axes in a complementary
manner: specification languages that can be used to define the performance
characteristics of algorithms; methodologies for algorithms to adapt to
changing resource availability or heterogeneity resource availability; and
methodologies for algorithms to change behavior predictably, based on data
and control inputs. Similarly, advances are need in execution platforms to
support dynamically adapting applications. Platforms capabilities and interfaces
need to be extended to include: interfaces to define and specify the
performance characteristics of the underlying execution platforms; ability to
reallocate resources in response to the changing needs of algorithms. DDDAS
algorithms stress dynamicity – symbiotically; DDDAS platforms should expose
interfaces that enable applications to sense and respond to resource
availability, and interfaces that expose control inputs and monitoring of the
DDDAS application behavior, to ensure their observability and controllability.

To support these highly dynamic and heterogeneous execution requirements,
Novel
directions
to
support
new runtime systems methods are needed providing capabilities for dynamic
dynamic
&adaptive
runtime:

adaptation of the application program, encompassing the heterogeneity of
“compiler-‐embedded-‐in-‐ high-end computational models and real-time components. The runtime needs
runtime”

to support adaptive mapping of such heterogeneous programs across multiple
…
interfaces
to
define
and
levels of platform heterogeneity with commensurate heterogeneity in the
respective operating systems, seamlessly integrated. The runtime must
specify
performance

manage these heterogeneous resources and satisfy at the same time the goal
characteristics
of
the

of achieving a desired application level quality of service (QoS), under stringent
underlying

execution

conditions of high-end computations coordinated with the real-time nature of
platforms;
ability
to
reallocate
data-acquisition and control aspects. Novel directions for such capabilities
resources
in
response
to
the
include “compiler-embedded-in-the-runtime”, which have shown promise.
changing
needs
of
Capabilities needed include new methods and application interfaces for
algorithms…
determining available resources requesting resources, supporting program

adaptivity, and at multiple levels of granularity, and defining and determining

level of quality of service.

Such requirements for autonomic runtime, and characteristics and capabilities
needed for such runtime systems, and the challenges to create such
capabilities have been discussed in 2006 DDDAS Report, and the reader is
referred to that report for further details. Since that time, the role of multicore
technologies as core engines in computational platforms has become more
prevalent. Multicore-based processors (MPUs- Multicore Processing Units) will
populate the high-end and mid-range platforms, and will also be the processing
engines in instrumentation systems, sensors and embedded controllers. This
aspect is very important for DDDAS environments. The systems software
needed to support dynamic and adaptive mapping of DDDAS applications and
dynamic runtime support requirements entail unprecedented levels of
challenges. However, there is a simplification along one dimension of this
complex software challenge, by the fact that the same kinds of basic
processors (MPUs) will populate the entire range of platforms; that is, the
same kinds of multicores (MPUs) will populate the high-end, and will be the
computational engines for the sensors and controllers in the instrumentation
components of a DDDAS application. For example, ideas, like “compiler-

14

embedded-in-the-runtime” can now be examined in the context of multicores
being the unifying engines across the wide array of computational and
instrumentation platforms.

push
5.4 InfoSymbiotics/DDDAS CyberInfrastructure Testbeds
the
collective
sets
of

technologies
constituting

DDDAS connects real-time measurement devices and special purpose data
CyberInfrastructures
to
new

processing systems with distributed applications executing on a range of
levels
…
to
support
resources, from mobile devices operating in ad-hoc networks to high-end
the
required
interoperability
platforms connected to national and international high-speed networks.
software
and
hardware
Supporting infrastructure for these environments must go beyond present,
SuperGrids
…

static computational grids and include integrated and autonomous components
New
generations
of
that ingest data and drive adaption at all levels. Here components can be for
CyberInfrastructure
example sensors, actuators, resource providers or decision makers; data can
Collaboratories
be real time, historical, filtered, fused or metadata; adaption can be applied at
present
all levels such as choosing resources or mediating between data sources.
DDDAS capabilities require software and hardware cyberinfrastructures
testbed
opportunities…

supporting SuperGrids[12] of computational platforms dynamically integrated

with the instrumentation platforms, and where such cyberinfrastructures
embody applications and systems software architectural frameworks that
support seamlessly the integration of this range of platforms, from the high-
end to the real-time data acquisition and control. These software frameworks
need to vertically integrate the systems’ layers, from the applications to the
hardware layers, and across computational and instrumentation components
(including sensors and controllers, and networks thereof). Moreover, there is
need to leverage horizontally (across different application examples or areas)
advances made on such software frameworks.

In thinking about future DDDAS infrastructures we observe that the existing
landscape provides a rich set of computational, networking and data systems
infrastructures. Broadly speaking DDDAS applications can be seen as exploiting
these capabilities but also posing high and differing demands in existing
computational infrastructures. High-performance computing resources, such
as for example the NSF TeraGrid and the planned XD and Blue Waters facilities,
they are targeted to support high-end users, with application models exhibiting
the highest levels of concurrency. There are many DDDAS applications with
DDDAS
connects
real-‐time
such characteristics and needs for high-performance capabilities. However,
measurement
devices
and
policy restrictions in resource management in these high-performance systems
special
purpose
data
have traditionally hindered the broad and regular use of shared HPC
processing
systems
with
environments for DDDAS applications, because these facilities use for example
distributed
applications
static batch queues, not suitable for the dynamic and interactive requirements
executing
on
a
range
of
of DDDAS High-throughput computing resources, such as for example the
resources,
from
mobile
devices
Open Science Grid which support interoperability across heterogeneous
operating
in
ad-‐hoc
networks
platforms, are possible computational infrastructures to be explored in DDDAS
to
high-‐end
platforms
environments.
connected
to
national
and

international
high-‐speed
In Section 4 of the present report, application examples and
cyberinfrastructure collaboratories are provided that are potential candidates
networks…

as testbeds for DDDAS. Other such cyberinfrastructure frameworks are being
…
require
software
and

created by several of DDDAS-supported projects, such as for example adverse
hardware
cyberinfrastructures
weather prediction (Droegemeier in [2]), in environmental monitoring and
…
computational,
networking
critical infrastructures cited elsewhere in this report, and also examples of
…
data
systems
infrastructures
industry applications, such as for example in seismic migration and inverse
problems which deal in addition with “big data”. The advances in DDDAS
applications modeling, in algorithms, and understanding errors and uncertainty
invoke additional requirements for robust and efficient infrastructure support,
for example operating at diverse time-scales, with concomitant increase in
potential for failures at all levels and fail-safe implementation requirements.

For InfoSymbiotics/DDDAS environments, infrastructure will need to address
myriad issues arising from diverse, dynamic data from different sources.

15

Integrating sensors into the DDDAS infrastructure will necessitate rethinking
network architectures to support new protocols for push-based data, and two
way communications to configure sensors based. Data in the DDDAS
infrastructure will be stored and accessed in new hierarchies based on locality,
filtering, quality control and other features. Experimental environments to
support DDDAS computing are available at different levels of production use.
The NSF sponsored Global Environment for Network Innovations (GENI) [17]
provides exploratory environments for research and innovation in emerging
global networks, and the NSF EAVIV [16] project provides a dynamically
configurable network testbed for high speed end-to-end connectivity with
TeraGrid resources. More recently, the NSF Future Grid [18] is being deployed
to allow researchers to tackle complex research challenges in computer science
related to the use and security of grids and clouds. Cloud computing is an
emerging infrastructure that builds upon recent advances in virtualization and
data-centers at scale to provide an on-demand capability. There are both
commercial clouds (EC2, Azure, IBM Deep-Cloud) and academic clouds (DoE
Science-Cloud, NSF Future Grid) that are viable infrastructure for DDDAS
applications. They provide different models for data-transfer, localization and
data-affinity. Consequently, exploring how the different data capabilities in
conjunction with the on-demand compute Clouds can be used, and in
combination with “traditional” grids to collectively support DDDAS applications,
are important open questions.

The underlying hardware platforms need to be elastic and able respond to
dynamic requirements. Persistent national infrastructure is envisioned as
needed as well as infrastructure that is portable and able to be quickly
deployed in the field to support medical, military and other application
scenarios. End-user connectivity must be addressed, connecting national
infrastructure to researchers in academic laboratories as well as to mobile
users and devices in the field. Infrastructure itself thus needs to be dynamically
require
configurable. A fundamental need for end resources supporting DDDAS,
new
whether storage, compute, network or data collecting, is that they support
supporting
resource
aware
dynamic provisioning which is flexible, adaptive and fine grained. This issue
and
resource
adaptive
involves both technical developments (e.g. such as the ION dynamic network
methodologies…
protocols [19]) along with appropriate policies to allow dynamic use of
include
capabilities
for
resources. Production resources focused on CPU utilization have the
application
software
evolution
technologies to provide dynamic use, but their usage models do not typically
allow for dynamic usage policies.
and
maintenance,
repositories

of
application
models
and

repositories
of
data,

knowledge-‐based
systems
for
5.5 InfoSymbiotics/DDDAS CyberInfrastructure Software Frameworks
application
characterization,

application
analytics,
InfoSymbioticSystems/DDDAS environments embody dynamic integration of
application
models
validation,
computational and instrumentation aspects of the application support system,
and
verification
and
testing…
and in fact DDDAS imply a unified computational-instrumentation platform for
an application, rather than traditional infrastructure approaches where the
computational platforms, while integrated with the archival data repositories,
they are viewed as distinct from the instrumentation platforms. Integration of
sensing, modeling and feedback, is the primary challenge in constructing
DDDAS capabilities; that is: integrating the loop from measurements to
predictions and feedback for highly complex systems, dealing with large, often
unstructured and streaming data. Thus, InfoSymbiotics/DDDAS require new
cyberinfrastructures supporting resource aware and resource adaptive
methodologies. Systems-software frameworks of interest here include
application programming environments, runtime, application composition and
problem solving environments. Application software frameworks for
InfoSymbiotics/DDDAS raise the level of requirements needed to include
capabilities for application software evolution and maintenance, repositories of
application models and repositories of data, knowledge-based systems for
application characterization, application analytics, application models
validation, and verification and testing.

16

Once dynamic behavior is provided at all levels of the infrastructure the
question becomes how can resources be provisioned and used by applications
and middleware. A common definition is needed to describe the quality of
service (QoS) provided by the resource. This description needs to include the
capabilities provided by the resource (e.g. bandwidth, memory, available
storage) along with usage characteristics (e.g. cost, security, reliability,
performance). Requirements for DDDAS systems overlap with known needs for
many complex end-to-end scientific applications. However, additional and
fundamental requirements are introduced to support dynamic data scenarios,
such as the ability to handle events, and the integration of temporal and
spatial awareness into the system at all levels necessary to support decision
making. Systems need to react swiftly and reliably to deal with faults and
failure to provide a guaranteed quality of service.

Autonomic capabilities are important at all levels to respond to the content of
dynamic data or changing environments. The need for autonomic capabilities
arise at many levels of DDDAS, for example, wherever dynamic execution and
adaptivity is required – models and algorithms, the software and systems
services, infrastructure capabilities; autonomic capabilities (such as behaviors
based upon planning & policy) provide an effective approach to manage the
adaptations and mechanics of dynamical behavior. In many DDDAS scenarios,
application workflows need to be dynamically composed and enacted based on
real-time data and changing objectives. An example includes an instrumented
hurricane modeling, which can achieve efficient and robust control and
DDDAS
connects
real-‐time

management of diverse model by dynamically completing the symbiotic
measurement
devices
and

feedback loop between measured data and a set of computational models.
special
purpose
data

processing
systems
with
Multiple coordination strategies in DDDAS infrastructures are essential to
distributed
applications
ensure meeting the highly stringent and dynamic requirements of such
executing
on
a
range
of
environments. DDDAS infrastructures need to support complex, intelligent
resources,
from
mobile
devices
applications using new programming abstractions and environments able to
operating
in
ad-‐hoc
networks
ingest and react to dynamic data. Initially, different infrastructures may be
to
high-‐end
platforms
needed for different application types, but the expectation is that there will be
connected
to
national
and
convergence and leverage of methods and technologies, if not universally
international
high-‐speed
across all application areas, at least among classes of such application areas.
networks…

With respect to testbed efforts, national, persistent DDDAS infrastructures
…
require
software
and
connecting new Petascale-range compute resources via 100 Gbps networks to
hardware
special purpose data devices could serve as testbed for a range of important
…
computational,
networking
and critical applications. Researchers operating in university and national or
industrial laboratories will require DDDAS testbeds that reliably and securely
…
data
systems
infrastructures

connect external data sources to institutional and distributed resources with
QoS guarantees and fault tolerance. Easily deployable and reliable systems
will be needed architected and implemented in the field over ad-hoc networks
to explore new DDDAS-based capabilities supporting medical, military, and
other applications, operating in special conditions.

Research opportunities are presented to provide persistent and fully featured
infrastructure, integrating frameworks, programming abstractions and
deployment methods into an overall architecture, developing common
APIs and schemas around which powerful tools can be provided, providing
methods for decomposing applications to take advantage of emerging
environments such as Clouds or GPUs in an integrated infrastructure, and
deploying persistent DDDAS infrastructure for research and production use.
Specific research challenges include: • Architecture: Application scenarios,
characteristics and canonical problems to drive infrastructure research and
development; Network architectures to support new protocols for sensor data
(push, pull, subscribe); Architecture of data hierarchy for dynamic data
processing and access; Integration of location and time awareness; • Tools:
Dynamic workflow tools building on above capabilities (unique demands: run
time environment, with changing services, events controlled workflows,
resource discovery, ...); Visualization, analysis and steering of large and

17

dynamic data (haptics, ...) for closed loop scenarios, real-time data, changing
characteristics, ... ; Security issues for sensors and autonomy, security issues
generally for new software; Execution environment supporting collaboration
and decision making (social networking), crowd-sourcing, citizen
engineering,... ; • Integration and Interoperability: How to define, carry and
operate on provenance information; Generalized interoperability, collaboration
and negotiation in decentralized decision making; Generalization of allocation
across different resources (networks, data ...), new methodologies of
allocation, ...; Negotiation mechanisms between applications and
infrastructure; Description for QoS (includes cost, availability, security,
performance, reliability, ...); More effective integration of computable
semantics throughout the infrastructure (e.g. tradeoff between simplicity and
expressiveness); Policies/cost models for dynamic resource allocation, resource
contention (e.g. for different applications); Integration with cloud computing to
take advantage of business model and scalability and collaboration,
virtualization, mutual collaboration between cloud computing and DDDAS

Developing DDDAS infrastructure is challenging, bringing issues related to
dynamic data that reach beyond those addressed in traditional grids that
require new and flexible policies along with comprehensive and integrated
services. The capabilities needed for the infrastructure are diverse, and
many facets have already been addressed in a diverse set of projects
across the different agencies which should be adopted where possible to
leverage knowledge advances and prevent duplication and/or re-invention.
Funding mechanisms need to be put in place that provide and support
complete, integrated, production infrastructure for broad DDDAS across
international and agency borders. Expectations for this infrastructure need
to be carefully thought out, so that appropriate outcomes are evaluated
rather than traditional metrics of utilization.

6. Learning and Workforce Development

InfoSymbiotics/DDDAS creates exciting multidisciplinary research opportunities
for undergraduate, graduate, and postdoctoral education and training. Given
creates

the recognition of InfoSymbiotics/DDDAS as a key scientific and technological
exciting
multidisciplinary

direction, and perhaps a new field, this presents a high potential for inspiring
training
opportinuties
…

students and attracting them into the many science and technology in

individual disciplines and in multidisciplinary experience involved in developing
…
developing
a
globally
DDDAS capabilities. The required research and technology efforts can bridge
competitive
workforce
…

academia and industry, providing more broadly trained academic and industry

workforce, or workforce in other parts of the private sector, as well as the
public sector. The industry sector has expressed interest in DDDAS, and
already partnerships between academe and research in industry have been
established for several DDDAS research projects. Also, the industry sector has
expressed the need for multidisciplinary educational experience as a key
element for their workforce. Research in the context of InfoSymbiotics/DDDAS
can create new alliances within and across departments, as well as cross-
institutional connections across academe, national laboratories, and industry,
nurturing relationships, enduring as students graduate and transition into the
workforce.

7. Multi-Sector, Multi-Agency Co-operation

InfoSymbiotics/DDDAS has engendered multidisciplinary research and
technology development across disciplines and in multi-institutional and multi-
sector, and multi-national collaborations. Such activities emerged initially as
seeding activities within broad agency programs, but the required synergism
was more systematically cultivated through the 2005 multi-agency program
solicitation which included co-operation from the EU-PF7 (Information Society

18

Technologies) Program and the UK e-Sciences Program. From early-on DDDAS
attracted the interest of the international community, and such collaborations
were encouraged and nurtured both in the projects that were created under
the DDDAS rubric but also through a broader community activities, including
the International DDDAS Workshop Series that have taken place yearly since
2003 (DDDAS/ICCS – www.dddas.org). The interest by industry is also
evident in the many projects which have created connections with industry,
especially the research arms of the industry sector. Overall, multidisciplinary
research and systematic support of multidisciplinary research, and in particular
…
systematic
support
of

under the umbrella of multi-agency collaborations, and connections with
multidisciplinary
research,
and
research in industry, behoove consideration from several perspectives as we
in
particular
under
the
move forward, and these are discussed in the case of InfoSymbiotics/DDDAS.

umbrella
of
multi-‐agency

collaborations,
and
In the US, DDDAS enticed interest and support from multiple stakeholders
connections
with
research
in
within a given agency as well as across agencies. The 2005 solicitation was
industry,
behoove
under the co-sponsorship of all NSF Directorates and the NSF International and
consideration
from
several
Small Business Offices, but also brought participation of NIH, NOAA, and
perspectives…
AFOSR, as well as international collaborations. The January 2006 DDDAS
Workshop that followed, included participation by several agencies: DHS, DOD
(OSD, JFCOM-J9, ONR, NRL, AFOSR), DOE (several National Laboratories),
NASA, NIH, NOAA, CIA, and NIST. The present workshop in addition to the
two co-sponsors, AFOSR and NSF, had participation from other parts of DOD
(AFRL, ARO, ARL, ONR, and NRL), DOE (Labs) DTRA, NASA, and NIH.

It’s a key item that multidisciplinary research cannot be funded through
fragmented and peripheral efforts. In recent years, there have been several
initiatives from various funding agencies to support research related to various
facets of DDDAS. The NSF ITR Program was a large 5-yr program with a
general and broad scope, aspects that were used to seed some efforts on
DDDAS, following the 2000 DDDASWorkshop. The more recent NSF Programs
CDI (on general software methods) and CPS (focused on embedded systems)
did not articulate the DDDAS vision to be considered by the community as
viable support sources for DDDAS-related research. The DOE PSAAP Program,
other recent DOE program calls on multi-scale research and Uncertainty
Quantification (UQ), and the UQ MURI of AFOSR, as well as some the NIH
RO1’s, also have certain flavor of multi-scale and data-driven research.
However, none of these programs has captured the full context of DDDAS and
the comprehensive scope of synergistic and multidisciplinary research that was
articulated and started with the 2005 multi-agency DDDAS Program
Solicitation, and which resulted in a rich set of coherent projects. The
solicitation inspired the community to embrace the DDDAS vision, and bring
together the requisite representation from the applications areas, computer
sciences, mathematics and statistics, and systems instrumentation, to open
new frontiers in the fundamentals and in new capabilities. The progress that
has resulted from these projects, as well as the increasingly wider realization of
the value of DDDAS [10,14], make it ever more imperative for renewed
programmatic efforts, and investments in DDDAS coordinated acros agencies
through joint program solicitations.

Multidisciplinary programs, supported in coordinated ways with other agencies
have been increasing in popularity, and have enticed overwhelming interest
from the research community. On one hand the research communities have
responded to cross-agency program calls on multidisciplinary research by
initiating cross-disciplinary teams and producing innovative proposals. On the
other hand it is known that multidisciplinary proposals and the ensuing projects
require a longer gestation and incubation period, because not only it is
challenging to bring together the multiple fields in the collaboration, but
typically there is a ramp-up stage for each project to establish the
communication and collaborative rapport across researchers from diverse
fields. There has been a challenge to establish stable, long-term funding on a
sustained basis. Several reports that have been produced over the recent
years, each make these points.

19

There are numerous benefits of coordination and joint efforts across agencies,
nationally, and in supporting synergistically such efforts. Multiple agencies
participating in coordinated and systematic efforts on DDDAS bring together
different research and technology communities and a diverse set of
stakeholders. Mission-oriented agencies can provide drivers and components,
has
leading to higher impact results: well-defined problems, clarity on the specific
excellent
track-‐record
of
decision information needed, feedback, access to key and realistic datasets and
other infrastructure, and research personnel from agency-supported Research
multistakeholder
interest
and

Laboratories that can participate in these interactions. Moreover, participation
support,
multidisciplinary

by several agencies as sponsors of a given project, leads to ownership of
efforts...

results and technology transfer of fundamental research. Finally, sponsorship
…academe-‐industry-‐ across agencies, leverages individual funding and contributes to more
government…
continuity and stability of sustaining the research for longer term.
higher
impact
results,…

well-‐defined
problems,
…
Likewise, involving the industrial sector in fundamental research engenders
clarity,
..
access
to
realistic
beneficial collaborations, brings to the research projects real-world data and
data
and
infrastructures…
infrastructure needed to validate the new ideas and research methods, and can
…ownership
of
results,

expedite technology transfer. In addition to participation in joint workshops,
…
technology
transfer,
joint research supported or catalyzed by government programs, and co-
increased
impact
of
sponsored by industry support can lead to beneficial and varying forms of
fundamental
research…

collaborations, driven by research efforts articulated in the preceding sections.

Some example priority areas include partnerships in the energy sector,
manufacturing, aerospace, telecommunications, medical, and information
technology/computer industry.

The 2006 DDDAS [2] Report provides additional context and examples of
benefits from cross-agencies and cross-sector joint efforts. The present
workshop endorsed the findings of that report, and reaffirmed the value of
coordinated efforts and including joint solicitations on InfoSymbiotics/DDDAS.

8. Summary

InfoSymbiotics/DDDAS provides the promise of new and exciting advances
with transformative impact. The concept is recognized as key to important
new capabilities, critical in many societal, commercial, and national and
international priorities and initiatives, identified in important studies, blue
ribbon-panels and other notable reports. The research required spans several
dimensions, and requires synergistic multidisciplinary thinking and
multidisciplinary research. Not only this is an opportunity of highly innovative
advances, but also an opportunity for developing a globally competitive
workforce. Advances made thus far, creating InfoSymbiotics/DDDAS
capabilities, add to this promise, albeit they have been achieved, through
limited and fragmented support. A diverse community of researchers has been
established over the years, drawing from multiple disciplines, and spanning
academe, industry research and governmental laboratories, in the US and
internationally. These research communities are highly energized, by the
success thus far, and by the wealth of ideas in confluence with other recent
technological advances.

In summary, InfoSymbiotics/DDDAS related opportunities and challenges,
involve synergistic multidisciplinary research in applications (for new methods
where simulations are dynamically integrated with real-time data acquisition
and control, and where application models are dynamically invoked), in
algorithms (where algorithms tolerant in their stability properties to
perturbations from streamed data, and algorithmic methods where uncertainty
quantification and error propagation methods can support efficiently error
propagation across dynamically invoked application models), in systems
software supporting such applications that exhibit dynamic execution
requirements (where models of the application are dynamically invoked, and

20

where high-end application models are dynamically integrated with the real-
time acquisition and control components of the application, and furthermore,
where the parts of the application that execute on the high-end side versus
…
those that execute at the sensors or controllers side, may vary during the
is
a
well-‐defined
concept,
execution of the application depending on dynamic requirements, and resource
…
new
and
exciting
advances
availability, e.g. data volume and bandwidth available). DDDAS drives these
with
transformative
impact…
kinds of needs and technology advances, provides leapfrogging opportunities
…well-‐defined
research
within the landscape of accelerating advances and emerging terrains in sensors
agenda…
and ubiquitous sensoring, in data collection and analysis, and in networking
research
spans
several
and computing. These advances create new platforms and environments for
dimensions…
supporting the complex systems of interest in societal, commercial, industrial
and national security settings, providing further motivation for embarking on
…drawing
from
multiple

comprehensive efforts for creating InfoSymbiotics/DDDAS capabilities.
disciplines,

research
communities
highly

energized
…
wealth
of
ideas…

The present report, as well as precedent DDDAS reports and in particular the
confluence
with
recent
January2006 DDDAS Report, they provide to the research communities a
technological
advances

wealth of science and technology challenges to be addressed in enabling
InfoSymbiotics/DDDAS capabilities. The reports also provide examples of
…
more
than
ever
timely
for

drivers and efforts on advances in a number of the challenges posed, such as
increased
efforts
on

on new methods and approaches that are needed in applications modeling
InfoSymbiotics/DDDAS!!!
methods, in mathematical and statistical algorithms, in systems software
supporting seamless integration of high-end computing with the real-time
data-acquisition and control systems, and in new instrumentation approaches
and capabilities, as well as the software architectural frameworks that embody
the DDDAS environments and the new kinds of cyberifrastructures that ensue,
the testbeds that need to be put in place for the comprehensive development
of the capabilities sought.

InfoSymbiotics/DDDAS is a well-defined concept, and a well-defined research
agenda has been articulated through this and previous workshops and reports.
All these, make timely the call for action, that was resoundingly expressed at
the August2010 DDDAS Workshop, for systematic support to embark in
pursuits for creating InfoSymbiotics/DDDAS capabilities, and to nurture the
research as well as the technology development, necessary to bring these
advances to the levels of maturity needed and enable the transformative
impact recognized as ensuing from InfoSymbiotics/DDDAS.

21

BIBLIOGRAPHY

[1] F. Darema, C. Douglas, A. Deshmukh: DDDAS 2000 Workshop; in
www.cise.nsf.gov/dddas

[2] G. Allen, K. Baldridge, G. Biros, A. Chaturvedi, C. C. Douglas, M. Parashar,
J. How, J. Saltz, E. Seidel, A. Sussman - (Editor. F. Darema):
DDDAS Workshop 2006 ; in www.cise.nsf.gov/dddas

[3] DDDAS/ICCS International Workshops Series www.dddas.org

[4] NSF Multiagency DDDAS Program Solicitation; in www.cise.nsf.gov/dddas

[5] Oden, J. T. ed., Simulation Based Engineering Science, Revolutionizing
Engineering Science Through Simulation, Report of the Blue Ribbon Panel on
Simulation Based Engineering Science:
http://www.nsf.gov/pubs/reports/sbes_final_report.pdf

[6] NSF 07-28, CYBERINFRASTRUCTURE VISION FOR 21ST CENTURY DISCOVERY
http://www.nsf.gov/pubs/2007/nsf0728/index.jsp?org=NSF

[7] National Science Foundation Vision for CyberInfrastructure for the 21st
Century (CIF21), http://www.nsf.gov/about/budget/fy2012/pdf/40_fy2012.pdf

[8] NSF/ACCI Taskforces on CyberInfrastructure for 21st Century
http://www.nsf.gov/od/oci/taskforces/index.jsp

[9] http://www.engineeringchallenges.org

[10] Technology Horizons - A Vision for Air Force Science & Technology During
2010-2030 http://www.af.mil/information/technologyhorizons.asp

[11] Dept. of Energy, ESnet Network Performance http://fasterdata.es.net

[12] F. Darema, SuperGrids, http://conferences.telecom-
bretagne.eu/data/asn-symposium/actes/18_Keynote_Darema_Supergrids.pdf

[13] F. Berman ed. Sustainable Economics for a Digital Planet
http://brtf.sdsc.edu/biblio/BRTF_Final_Report.pdf

[14] August2010DDDAS Workshop – Opening Remarks and Keynote
Presentations, http://www.dddas.org/AFOSR-NSFworkshop2010-plenary.html

[16] Strategies for Remote Visualization on a Dynamically Configurable
Testbed - The eaviv Project
http://www.cct.lsu.edu/CCT-TR/CCT-TR-2009-18

[17] Global Environment for Network Innovations
http://www.geni.net/?p=1984

[18] https://portal.futuregrid.org/

[19] http://www.internet2.edu/ion/

22

Appendix-0 Workshop Agenda

Day 1, Monday, August 30, 2010
7:30am - 8:15am Registration and Refreshments
8:15am - 9:00am Workshop Welcome
Opening Remarks
Dr. Werner Dahm, Chief Scientist, US Air Force
Introductory Remarks by AFOSR and NSF Leadership, and Co-Chairs
Dr. Frederica Darema, Director, Math, Info and LifeSciences Directorate, AFOSR
Dr. Ed Seidel, Assistant Directorate, Math and Physical Sciences Directorate, NSF
Prof. Craig Douglas, University of Wyoming
Prof. Abani Patra, SUNY-Buffalo
9:15am - 12:30pm Plenary Presentations
9:15am - 9:45 am Prof. J. Tinsley Oden, Univ. of Texas, Austin
A Dynamic Data-Driven System for Optimized Laser Treatment of Prostate Cancer
9:45am - 10:15am Prof. Kelvin K. Droegemeier, Univ. of Oklahoma
DDDAS Applied to High-Impact Local Weather: The LEAD Project
10:15am - 10:30am Break
10:30am - 11:00am Prof. Charbel Farhat, Stanford University and Dr. John Michopoulos, Naval
Research Laboratory
DDDAS for Material Characterization, Health Monitoring, and Critical Event
Prediction of Complex Structures
11:00am - 11:30am Prof. George E. Karniadakis, Brown University
Predictability and Uncertainty in DDDAS
11:30am - 12:00pm Dr. Sangtae Kim, Morgridge Institute of Research
Is Life a Dynamic Data Driven DNA Application System?
12:00pm - 12:30pm Prof. Patrick Jaillet, MIT
Data-Driven Optimization: Illustrations, Opportunities, Some Results, Key
Challenges
12:30pm - 1:30pm Working Lunch
1:30pm - 2:00pm Working Group Session
3:30pm - 3:45pm Break
3:45pm - 5:00pm Discussion of Summary Presentations
5:45pm Adjourn for the day

Day 2, Tuesday, August 31, 2010
8:15am - 8:30am Refreshments
8:30am - 10:00am Working Group Session
10:00am - 10:15am Break
10:15am - 12:00pm Working Group Session
12:00pm - 1:00pm Working Lunch
1:00pm - 3:00pm Working Group Outbriefing
3:00pm - 3:30pm Concluding Discussion
3:30pm Workshop Ends
3:30 pm - 3:45pm Break
3:45 pm - 5:00pm Meeting Only with Working Group Chairs and Organizers

Day 3, Wednesday, September 1, 2010
Initial Write-up of the Report by Working Group Chairs and Organizer

Appendix-1 Plenary Speakers

Professor Kelvin K. Droegemeier, University of Oklahoma
Professor Droegemeier is Vice President for Research, Regents' Professor of Meteorology, Weathernews
Chair Emeritus in Applied Meteorology and Roger and Sherry Teigen Presidential Professor at the University
of Oklahoma. In 2004, Dr. Droegemeier was appointed by President George W. Bush to a 6-year term on
the National Science Board, the governing body of the National Science Foundation that also provides
science policy guidance to the Congress and President. He presently chairs the Board’s Committee on
Programs and Plans. Dr. Droegemeier was co-founder in 1989 of the NSF Science and Technology Center
(STC) for Analysis and Prediction of Storms (CAPS), and served for five years as its deputy director. He then
directed CAPS from 1994 until 2006, and today CAPS is recognized around the world as the pioneer of
storm-scale numerical weather prediction. He is also the Director of the Sasaki Institute, a non-profit
organization that fosters the development and application of knowledge, policy, and advanced technology in
the government, academic and private sectors. As director of the CAPS model development project for 5
years, he managed the creation of a multi-scale numerical prediction system that has helped pioneer the
science of storm-scale numerical forecasting. This computer model was a fina list for the 1993 National
Gordon Bell Prize in High Performance Computing. In 1997, Dr. Droegemeier received the Discover Magazine
Award for Technology Innovation (computer software category), and also in 1997 CAPS was awarded the
Computerworld Smithsonian Award (science category). Droegemeier also is a recipient of the NSF Pioneer
Award and the Federal Aviation Administration's Excellence in Aviation Award. Dr. Droegemeier is a national
leader in the creation of partnerships among academia, government and industry. He initiated and led a 3-
year, $1M partnership with American Airlines to customize weather prediction technology for commercial
aviation, and this resulted in him founding a private company, Weather Decision Technologies, Inc., located
in Norman, that is commercializing advanced weather technology developed by the University of Oklahoma
and other organizations. The success with American Airlines also played a role in the establishment in
Oklahoma of the Aviation Services Division of Weathernews, the world's largest private weather company.
Dr. Droegemeier led a $10.6M research alliance with Williams Energy Marketing and Trading Company in
Tulsa, which is the largest such partnership between a university and a private company in the field of
meteorolo gy. He initiated and led the Collaborative Radar Acquisition Field Test (CRAFT), a national project
directed toward developing strategies for the real time delivery of NEXRAD radar data via the Internet.
CRAFT won two awards from the National Oceanic and Atmospheric Administration, and its success led the
National Weather Service to adopt its Internet data delivery strategy. As a follow-on to CRAFT, Droegemeier
established Integrated Radar Data Services (IRaDS) at OU, which is a National Weather Service-designed
top-tier provider of NEXRAD radar data to private industry. He has served as an associate editor for Mo nthly
Weather Review for 6 years served on the UCAR University Relations Committee, the last two as chair.
Elected to the UCAR Board of Trustees in 2002 and as its Vice Chairman in 2003, he became Chairman of
the Board in 2004. Dr. Droegemeier has served as a consultant to Honeywell Corporation, American Airlines,
the National Transportation Safety Board, and Climatological Consulting Corp. Dr. Droegemeier has
graduated 27 students and served on the committees of numerous others. Dr. Droegemeier's research
interests lie in thunderstorm dynamics and predictability, variational data assimilation, mesoscale dynamics,
computational fluid dynamics, massively parallel computing, and aviation weather.

Professor Charbel Farhat, Stanford University
Professor Farhat has been designated by the Institute for Science Information (ISI) as one of the most
highly cited researchers in engineering. He is the recipient of numerous prestigious awards including the
American Institute of Aeronautics and Astronautics (AIAA) Structures, Structural Dynamics and Materials
Award (2010), the United States Association of Computational Mechanics (USACM) John von Neumann
Medal (2009), the Institute of Electrical and Electronics Engineers (IEEE) Computer Society Gordon Bell
Award (2002), the International Association of Computational Mechanics (IACM) Computational Mechanics
Award (2002), the (AIAA) Rocky Mountain Section Engineer of the Year Award (2001), the Department of
Defense Modeling and Simulation Award (2001), the USACM Medal of Computational and Applied Sciences
(2001), the IACM Award in Computational Mechanics for Young Investigators (1998), the USACM R. H.
Gallagher Special Achievement Award for Young Investigators (1997), the IEEE Computer Society Sidney
Fernbach Award (1997), the IBM Sup'Prize Achievement Award (1995), the American Society of Mechanical
Engineers (ASME) Aerospace Structures and Materials Best Paper Award (1994), the Society of Automotive
Engineers (SAE) Arch T. Colwell Merit Award (1993), the CRAY Research Award (1990), a TRW fellowship
(1989), the United States Presidential Young Investigator Award (1989), and the Control Data Corporation
PACER Award (1987). He is a Fellow of the American Society of Mechanical Engineers (2003), Fellow of the
International Association of Computational Mechanics (2002), Fellow of the World Innovation Foundation
(2001), Fellow of the United States Association of Computational Mechanics (2001), and Fellow of the
American Institute of Aeronautics and Astronautics (1999). He has been an AGARD lecturer on aeroelasticity
and computational mechanics at several distinguished European institutions, and a keynote speaker at

24

numerous international scientific meetings. He serves as Editor of the International Journal for Numerical
Methods in Engineering and serves on the editorial boards of eleven other international scientific journals.
He also serves on the U.S. Bureau of Industry and Security's Emerging Technology and Research Advisory
Committee (ETRAC) at the U.S. Department of Commerce, and on the technical assessment boards of
several national research councils and foundations.

Professor Patrick Jaillet, MIT
Professor Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and
Computer Science and a member of the Laboratory for Information and Decision Systems at MIT. He is also
Co-Director of the MIT Operations Research Center. He was Head of Civil and Environmental Engineering at
MIT from 2002 to 2009, where he currently holds a courtesy appointment. From 1991 to 2002 he was a
professor at the University of Texas in Austin, the last five years as the chair of the Department of
Management Science and Information Systems. He co-founded and was director of UT's Center for
Computational Finance. Before his appointment at UT Austin, he was a faculty and a member of the center
for applied mathematics at the Ecole Nationale de Ponts et Chaussee in Paris. He received a Diplome
d'Ingenieur from France (1981), then came to MIT where he received the SM in Transportation (1982)
followed by a PhD in Operations Research (1985). Dr. Jaillet's research interests include on-line problems;
real-time and dynamic optimization; network design and optimization; probabilistic combinatorial
optimization; and financial engineering. His research has been funded by NSF, ONR, USDOT, and from
private funds (e.g., UPS, Indosuez Bank). Professor Jaillet has taught courses in combinatorial optimization;
network optimization; probabilistic methods in operations research; stochastic analysis; risk management;
and mathematics in finance. Dr. Jaillet's consulting works include supply chain strategy, logistics and
distribution optimization, electronic marketplace design, and development of optimization solutions in
various industries, including automotive, financial and manufacturing. Dr. Jaillet was a Fulbright Scholar in
1990. He is a member of the Institute for Operations Research and Management Science Society (INFORMS)
and of the Society for Industrial and Applied Mathematics (SIAM). He is currently an Associate Editor for
Networks, Transportation Science, and Naval Research Logistics, and has been an Associate Editor for
Operations Research from 1994 until 2005.

Professor George Em Karniadakis, Brown University
Professor George Karniadakis received his S.M. (1984) and Ph.D. (1987) from Massachusetts Institute of
Technology. He was appointed Lecturer in the Department of Mechanical Engineering at MIT in 1987 and
subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princeton
University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as
Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at
Caltech (1993) in the Aeronautics Department. He joined Brown University as Associate Professor of Applied
Mathematics in the Center for Fluid Mechanics on January 1, 1994. He became a full professor on July 1,
1996. He has been a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT since
September 1, 2000. He was Visiting Professor at Peking University (Fall 2007). He is a Fellow of the Society
for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-),
Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American
Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the CFD award (2007) by the US
Association in Computational Mechanics. His research interests include diverse topics in computational
science both on algorithms and applications. A main current thrust is stochastic simulations and multiscale
modeling of physical and biological systems (especially the brain).

Professor Sangtae Kim, Morgridge Institute for Research
Dr. Kim is a member of the National Academy of Engineering and a fellow of the American Institute of
Medical and Biological Engineers. His research citations include the 1993 Allan P. Colburn Award of the
American Institute of Chemical Engineers, the 1992 Award for Initiatives in Research from the National
Academy of Sciences and a Presidential Young Investigator award from NSF in 1985. His treatise,
Microhydrodynamics, first published in 1991, is considered a classic in that field and was recently selected
by Dover Publications for its reprint series. He has an active record of service on science and technology
advisory boards of government agencies, the U.S. National Research Council and companies in IT-intensive
industries. Despite significant administrative roles in public service, his research activities remain significant
and lie at the intersection of applied mathematics, biological sciences, and informatics. One program exploits
biomimetic, fluidic self assembly to contribute to the roadmap for the "one-cent" RFID tag. A second
program leverages his leadership experiences in the pharmaceutical industry to help create the emerging
discipline of pharmaceutical informatics and a pathway for the pharma industry to harvest the fruits of
genomics. A third program combines his experiences in academic/industrial research, IT management, and
public service, to create new information architectures (the cyberinfrastructure) for rapid-response
manufacturing supply chains.

25

Dr. John Michopoulos, Naval Research Laboratory
Dr. Michopoulos is a Research Scientist/Engineer and director of Computational Multiphysics Systems Lab
(CMSL) of the Center of Computational Materials Sciences at the Naval Research Laboratory (NRL), Dr.
Michopoulos oversees multiphysics and information technology research and development, operations and
initiatives. Current major initiatives include research and development of linking performance to material
through dynamic data and specification driven methodologies, electromagnetic launcher dissipative
mechanism modeling and simulation, heterogeneous integrated computational, sensing and communication
grids via data-driven multidisciplinary and holistic approaches and environments, engineering sciences
research, development and management in areas of computational, theoretical and experimental
multiphysics, platform/structure simulation based design, mechatronic/robotic data-driven characterization
of continua, automation of research, distributed supercomputing, and multiphysics design optimization. Dr.
Michopoulos also currently serves as the vice-chair of the Computers and Information in Engineering
Division of the American Society of Mechanical Engineers. He is an associate editor for the Journal of
Computers and Information Science in Engineering and the Journal of Computational Sciences. He is a
founding member and chair of the International Science and Technology Outreach Society and prior to
joining NRL he has been a senior research scientist for Geo-Centers Inc and prior to that director of the
Image Processing Laboratory of the Institute of Fracture and Solid Mechanics at Lehigh University. He has
participated in several blue ribbon panels including the tri-services Workshop on SHM, November 17, 2008 b
Thu, November 20, 2008, Austin TX. He has also consulted for various companies and research
organizations and has authored and co-authored more than 210 publications and books and has been
honored with more than 47 awards. Dr. Michopoulos holds an electrical and civil engineering degrees and a
Ph.D. in Applied Mathematics and Theoretical Mechanics from the National Technical University of Athens,
and has pursued post-doctoral studies at Lehigh University on computational multi-field modeling of
continuum system.

Professor J. Tinsley Oden, University of Texas-Austin
Professor Oden is the Associate Vice President for Research, the Director of the Institute for Computational
Engineering and Sciences, the Cockrell Family Regents' Chair in Engineering #2, the Peter O'Donnell Jr.
Centennial Chair in Computing Systems, a Professor of Aerospace Engineering and Engineering Mechanics
and a Professor of Mathematics at The University of Texas at Austin. Oden has been listed as an ISI Highly
Cited Author in Engineering by the ISI Web of Knowledge, Thomson Scientific Company. His work was key to
establishing computational mechanics as a new intellectually rich discipline that was built upon deep
concepts in mathematics, computer sciences, physics, and mechanics. Computational Mechanics has since
become a fundamentally important discipline throughout the world, taught in every major university, and
the subject of continued research and intellectual activity. Dr. Oden is an Honorary Member of the American
Society of Mechanical Engineers and is a Fellow of six international scientific/technical societies: IACM, AAM,
ASME, ASCE, SES, and BMIA. He is a Fellow, founding member, and first President of the U.S. Association
for Computational Mechanics and the International Association for Computational Mechanics. He is a Fellow
and past President of both the American Academy of Mechanics and the Society of Engineering Science.
Among the numerous awards he has received for his work, Dr. Oden was awarded the A. C. Eringen Medal,
the Worcester Reed Warner Medal, the Lohmann Medal, the Theodore von Karman Medal, the John von
Neumann medal, the Newton/Gauss Congress Medal, and the Stephan P. Timoshenko Medal. He was also
knighted as "Chevalier des Palmes Academiques" by the French government and he holds four honorary
doctorates, honoris causa, from universities in Portugal (Technical University of Lisbon), Belgium (Faculte
Polytechnique), Poland (Cracow University of Technology), and the United States (Presidential Citation, The
University of Texas at Austin). Dr. Oden is a member of the U.S. National Academy of Engineering and the
National Academies of Engineering of Mexico and of Brazil. His current research focuses on the subject of
multi-scale modeling and on new theories and methods his group has developed for what they refer to as
adaptive modeling. The core of any computer simulation is the mathematical model used to study the
physical system of interest. They have developed methods that estimate modeling error and adapt the
choices of models to control error. This has proven to be a powerful approach for multi-scale problems.
Applications include semiconductors manufacturing at the nanoscale. Dr. Oden, along with ICES researchers,
is also working on adaptive control methods in laser treatment of cancer, particular prostate cancer. This
work involves the use of dynamic-data-driven systems to predict and control the outcome of laser
treatments using adaptive modeling strategies.

26

Appendix-2 List of Registered Participants

NON GOVERNMENT Partcipants

Adam Bojanczyk Cornell University
Gabrielle Allen Louisiana State University
Jeff Anderson NCAR
Ron Askin Arizona State University
Siva Banda Air Force Research Laboratory
Kirstie Bellman The Aerospace Corporation
Dennis Bernstein University of Michigan Aerospace Eng. Dept
George Biros Georgia Institute of Technology
Alok Chaturvedi Purdue University
YangQuan Chen Utah State University
Janice Coen NCAR
Li Deng University of Wyoming
Yu Ding Texas A&M
Craig Douglas University of Wyoming Mathematics Department
Kelvin Droegemeier University of Oklahoma
Tony Drummond Lawrence Berkeley National Lab
Johnny Evers Flight Vehicle Integration, AFRL/RWAV
Yusheng Feng University of Texas at San Antonio
Paul Flikkema Northern Arizona University
Jose Fortes University of Florida
David Fuentes The University of Texas MD Anderson Cancer Center
Tryphon Georgiou University of Minnesota
Omar Ghattas University of Texas at Austin
Adom Giffin Princeton University
Leana Golubchik University of Southern California
Chuck Hansen University of Utah
Salim Hariri The University of Arizona
Don Hearn University of Florida
Chris Hill MIT
Vasant Honavar Iowa State University
Jonathan How MIT
Xiaolin Hu Georgia State University
Marty Humphrey Department of Computer Science, University of Virginia
Patrick Jaillet MIT
Shantenu Jha Louisiana State University
Geroge Karniadakis Brown
Tim Kelley North Carolina State University
Yannis Kevrekidis Princeton University
Sang Kim Morgridge Institute for Research
MJ Kramer The Aerospace Corporation
Tahsin Kurc Center for Comprehensive Informatics, Emory University
Craig Lee The Aerospace Corporation

27

Gregory Madey University of Notre Dame
Kumar Mahinthakumar North Carolina State University
Amit Majumdar San Diego Supercomputer Center
Bani Mallick Texas A&M
William (Mac) McEneaney UC San Diego
Dimitris Metaxas Rutgers University
John Michopoulos Naval Research Laboratory
Fairul Mohd-Zaid 711 Human Performance Wing, RHCV
Jarek Nabrzyski Center for Research Computing
Lewis Ntaimo Texas A&M
J. Tinsley Oden University of Texas at Austin
Srini Parthasarathy Ohio State University
Abani Patra University at Buffalo
Jin-Song University of Oklahoma, School of CEES
E. Bruce Pitman University at Buffalo
Serge Prudhomme ICES, UT Austin
Guan Qin University of Wyoming
Anand Ranganathan IBM TJ Watson Research Center
Sai Ravela MIT
Joel Saltz Emory University
Adrian Sandu Virginia Tech
Puneet Singla University at Buffalo
Young-Jun Son The University of Arizona
Vaidy Sunderam Emory University, Math & CS
Alex Szalay Johns Hopkins University
Mario Sznaier Northeastern University
Georgios Theodoropoulos University of Birmingham
Carlos A. Varela Rensselaer Polytechnic Institute
Anthony Vodacek Rochester Institute of Technology
Gregor von Laszewski Indiana Univerity, Pervasive Technology Institute
Tim Wildey University of Texas at Austin - ICES
Dongbin (D.B.) Xiu Purdue University
David Fuentes University of Texas
Victor Giurgiutiu University of South Carolina
Milton Halem UMBC
Dinesh Manocha UNC Chapel Hill
Andreas Terzis Johns Hopkins University
Srinidhi Varadarajan Virginia Tech
Jon Weissman University of Minnesota

GOVERNMENT AGENCIES Participants

Van Blackwood AFOSR
Bob Bonneau AFOSR
Stephanie Bruce AFOSR
Patrick Carrick AFOSR
Milt Corn NIH/NLM
Frederica Darema AFOSR

28

Jason Davis AFOSR
Maj Michelle Ewy AFOSR
Fariba Fahroo AFOSR
Jonathan Griffin AFOSR
John Hannan DTRA
Tom Henderson NSF/CISE
Tom Hussey AFOSR
Kiki Ikossi DTRA
Suhada Jayasuriya NSF
Scott Harper ONR
Robert Kozma AFRL/RYHE
Lee Jameson NSF/MPS
David Luginbuhl AFOSR
John Luginsland AFOSR
Kim Luu AFRL/RDSM
George Maracas NSF/ENG
Peter McCartney NSF/BIO
Joe Mook NSF/OISE
Manish Parashar NSF/OCI
Leonid Perlovsky AFRL/RYHE
Kitt Reinhardt AFOSR
Stan Rifkin AFOSR
Steve Rogers AFRL/RY
Janet Spoonamore ARO
David Stargel AFOSR
S. Ananthram ARL
Ralph Wachter ONR
H. E. Seidel NSF/MPS
Kishan Baheti NSF/ENG
Demetrios Kazakos NSF/EHR
Terry Lyons AFOSR
D. J. Mook NSF/OISE
Tristan Nguyen ONR
Michael Seablom NASA
Doug Smith NSF/ENG
Mitat Birkan AFOSR

29

Appendix-3

All WGs - Common and overarching Issues
All WGs should address the following common and overarching issues:
o The scope of research challenges is clearly wide and in need of fundamental advances. Why is now
the right time for fostering this kind of research?
o What are the Grand S&T Challenges in enabling DDDAS? What are ongoing research advances can
be used as leverage and springboard to enable DDDAS?
(Each WG will address the research challenges and opportunities)
o What kinds of processes, venues and mechanisms are optimal to facilitate the multidisciplinary
nature of the research needed in enabling such capabilities?
o What past or existing initiatives can contribute, and what new ones should be created to
systematically support such efforts?
o What are the benefits of coordination and joint efforts across agencies, nationally and in supporting
synergistically such efforts?
o What kinds of connections with the industrial sector can be beneficial? How can these be fostered
effectively to focus research efforts and expedite technology transfer?
o How these new research directions can be used to create exciting new opportunities for
undergraduate, graduate and postdoctoral education and training?
o What novel and competitive workforce development opportunities can ensue?
o What National and International critical challenges are addressed through DDDAS capabilities?

WG1 – Algorithms and Data Assimilation
(Janice Coen and George Biros)
DDDAS environments require algorithms, mathematical and statistical, both numeric and non-numeric, that
have good convergence properties under perturbations from streamed data into the executing application.
DDDAS goes beyond the traditional data-assimilation approaches:
o What is the state-of-the-art and what are the challenges in the applications algorithms to enable
such capabilities for the applications models/simulations?
o What algorithms’ development is needed to enable application algorithms tolerant to perturbations
from “on-line” input data, and with good stability properties?
o How can one select and incorporate dynamically appropriate algorithms as the application
requirements and data sets change in the course of the simulation?
o What kinds of approaches, such as knowledge-based systems, can be employed, and what
interfaces and applications assists are needed to allow such capabilities?
o What systems support is required to develop such environments?
o How do the existing methods and capabilities in the above need to be advanced?

WG2 - Uncertainty Quantification and MultiScale Modeling
(Bani Mallick, Dongbin Xiu)
DDDAS environments entail application models that can interface and dynamically interact with the
measurement data systems (archival, real-time data acquisition and control systems). Such interaction
entails dynamic application models and application components, at runtime, as dictated by the streamed
data, and can include dynamic invocation of models at multiple scales – that is “dynamic multi-scale”.
Models, experiments and observations are all representations and discrete samples of behavior. Quantifying
and managing the outcomes of application systems (predictions, control actions, …) must account for these
uncertainties. Such situations ensue new and increased challenges, beyond the traditional multi-scale, and
uncertainty quantification considerations.
o What are the overall opportunities and challenges in DDDAS applications modeling?
o What research and technologies are covered by the present projects?
o As DDDAS requirements are expected to be dynamic, what are the implied applications modeling
technology advances that are need and what’s the needed systems support?
o What is special if you have a multiscale/multiphysics system? How do you do deal with multimodal
data?
o What methodologies from the emerging field of UQ are applicable here, and in particular in the case
where models of other components of the application are dynamically invoked? Conversely what
new developments are needed to enable the use of dynamic data and simulations especially for
complex systems? What are the issues in data management, dynamic selection of application
components, mapping, interfaces for request and allocation of systems resources so that quality of
service is ensured for the applications?

30

o Provide applications examples that will benefit from the new paradigm, existing and potential new
applications, challenges in developing such applications, multilevel and multimodal modeling,
composition of such complex applications, data management and interfaces to experiments/field-
data, computation, memory and I/O requirements.

WG3 - Large and Heterogeneous Data from Distributed Measurement & Control Systems
(Alok Chaturvedi, Adrian Sandhu)
DDDAS inherently involves large amounts of data that can result from heterogeneous and distributed
sources, collected in differing time-scales and in different formats, and which need to be preprocessed
before automatically integrating them to the executing applications that need use the new data.
o What is the state of the art in measurement systems and how are they integrated in DDDAS, where
measurements from sensors, other instruments and data repositories are dynamically integrated
with the application modeling to improve the application modeling?
o Conversely, what is the state of the art in on-line application control of the measurement instrument
or process providing opportunity to improve the measurement process, guide the design and
operational aspects of measurement instruments, and networks of distributed heterogeneous
sensors and networks of embedded controllers?
o What are the methods that need to be developed to guide the architecture of sets of sensors and
other instruments thus improving the effectiveness or efficiency of the measuring systems, and
networks of distributed heterogeneous sensors and networks of embedded controllers?
o What are the challenges and opportunities in software and hardware technologies to enable such
dynamic interfaces to such measurement and control systems, and their associated data sets?
What improvements in the methods are expected, how are they going to be enabled?
o How the existing methods and capabilities in all the above need to be advanced?

WG4 - Building an Infrastructure for DDDAS
(Gabrielle Allen, Shantenu Jha)
DDDAS integrates real-time sensor and other measurement devices with special purpose data processing
systems together with the parts of the application that execute in larger platforms and driving a seamless
integration of stationary and mobile devices together with large high-end platforms, entailing grids that go
beyond the present computational grids.
o What are the challenges in the infrastructure just described above?
o What are the challenges and opportunities in software and hardware technologies to enable such
dynamic interfaces?
o What improvements in the measurement methods are expected and how are they going to be
enabled?

WG5 - Systems Software
(Srinidhi Varadarajan, Dinesh Manocha)
Quality of service, program software environments, data massaging, network security, and availability of
common libraries are all important to making a DDDAS work in a global manner.
o What is the state-of-the-art and what advances are needed in algorithms and software and what
new capabilities need to be provided by the underlying systems and platforms on which these
applications execute, so that quality of service is ensured?
o What are the software challenges in the programming environments for the development and
runtime support, under conditions where the underlying resources as well as the applications
requirements might be changing at execution time?
o What are the issues in data management, dynamic selection of components, dynamic invocation of
components, mapping to underlying resources, interfaces for request, and allocation of systems
resources so that quality of service is ensured for the applications?
o What are the additional capabilities that are needed in the application support and systems
management services?
o How can these be fostered effectively to focus research efforts and expedite technology transfer

31

Projects Under the DDDAS Rubric

Projects Under the DDDAS Rubric

InfoSymbiotics/DDDAS: The Power of Dynamic Data Driven Applications Systems

More Related Content

Similar to InfoSymbiotics/DDDAS: The Power of Dynamic Data Driven Applications Systems (20)

More from The Air Force Office of Scientific Research (20)

Recently uploaded (20)

InfoSymbiotics/DDDAS: The Power of Dynamic Data Driven Applications Systems