Dynamic Data Driven Application Systems
(DDDAS)
A new paradigm for
applications/simulations
and
measurement methodology
… and how it would impact CyberInfrastructure!
Dr. Frederica Darema
Senior Science and Technology Advisor
Director, Next Generation Software Program
1
NSF
What is DDDAS
Measurements
Experiment
Field-Data
User
2
(Symbiotic Measurement&Simulation Systems)
Experiment
Measurements
Field-Data
(on-line/archival)
User
Challenges:
Application Simulations Development
Algorithms
Computing Systems Support
Examples of Applications benefiting from the new paradigm
• Engineering (Design and Control)
– aircraft design, oil exploration, semiconductor mfg, structural eng
– computing systems hardware and software design
(performance engineering)
• Crisis Management and Environmental Systems
– transportation systems (planning, accident response)
– weather, hurricanes/tornadoes, floods, fire propagation
• Medical
– customized surgery, radiation treatment, etc
– BioMechanics /BioEngineering
• Manufacturing/Business/Finance
– Supply Chain (Production Planning and Control)
– Financial Trading (Stock Mkt, Portfolio Analysis)
DDDAS has the potential to revolutionize
science, engineering, & management systems
3
NSF March 2000 Workshop on DDDAS
(Co-Chairs: Craig Douglas, UKy; Abhi Desmukh, UMass)
Invited Presentations
• New Directions on Model-Based Data Assimilation (Chemical Appl’s)
Greg McRae, Professor, MIT
• Coupled atmosphere-wildfire modeling
Janice Coen, Scientist, NCAR
• Data/Analysis Challenges in the Electronic Commerce Environment
Howard Frank, Dean, Business School, UMD
• Steered computing - A powerful new tool for molecular biology
Klaus Schulten, Professor, UIUC, Beckman Institute
• Interactive Control of Large-Scale Simulations
Dick Ewing, Professor, Texas A&M University
• Interactive Simulation and Visualization in Medicine: Applications to
Cardiology, Neuroscience and Medical Imaging
Chris Johnson, Professor, University of Utah
• Injecting Simulations into Real Life
Anita Jones, Professor, UVA
Workshop
Report: www.cise.nsf.gov/dddas
4
PETROLEUM APPLICATIONS
SALT
DOME
FAULT
GAS
OIL
WATER
Surface hydrophone array
6
Fire Model
• Sensible and latent heat
fluxes from ground and
canopy fire -> heat fluxes
in the atmospheric model.
• Ground heat flux used to
dry and ignite the canopy.
• Fire’s heat fluxes are
absorbed by air over a
specified extinction depth.
• 56% fuel mass -> H20 vapor
• 3% of sensible heat used
to dry ground fuel.
Kirk Complex Fire. U.S.F.S. photo
7
Slide Courtesy of Coen/NCAR
Coupled atmospheric and wildfire models
8
Slide Courtesy of Coen/NCAR
AMAT Centura Chemical Vapor Deposition Reactor
Operating Conditions
Reactor Pressure
Inlet Gas Temperature
Surface Temperature
Inlet Gas-Phase Velocity
1 atm
698 K
1173 K
46.6 cm/sec
Surface Reactions
Gas Phase Reactions
SiCl3H  HCl + SiCl2
SiCl2H2  SiCl2 + H2
SiCl2H2  HSiCl + HCl
H2ClSiSiCl3  SiCl4 + SiH2
H2ClSiSiCl3  SiCl3H + HSiCl
H2ClSiSiCl3  SiCl2H2 + SiCl2
Si2Cl5H  SiCl4 + HSiCl
Si2Cl5H  SiCl3H + SiCl2
Si2Cl6  SiCl4 + SiCl2
SiCl3H + 4s  Si(B) + sH + 3sCl
SiCl2H2 + 4s  Si(B) + 2sH + 2sCl
SiCl4 + 4s  Si(B) + 4sCl
HSiCl + 2s  Si(B) + sH + sCl
SiCl2 + 2s  Si(B) + 2sCl
2sCl + Si(B)  SiCl2 + 2s
H2 + 2s  2sH
2sH  2s + H2
HCl + 2s  sH + sCl
sH + sCl  2s + HCl
Slide Courtesy of McRae/MIT
9
MSTAR
(DARPA)
(Moving and Stationary Target Acquisition and Recognition)
TREES
Focus of
Attention
H2O
Index Database
(created off-line)
GRASS
ROAD
...
Segmented
Terrain Map
..
.
Regions of
Interest (ROI)
Target
& Clutter
Database
SAR Image &
Collateral Data
- DTED, DFAD
- Site Models
- EOSAT imagery
Search Tree
ROI Hypothesis
y
GRASS

BMP2
Indexing
Local
Scene Map
x
Target & Scene
Model Database
(created off line)
TREES
TREES
Task Predict
Task Extract
ROI Hypothesis
Predict
Shadow
(?)
y

BMP-2
x
CAD
TREES
GRASS
TREES
Statistical
Model
Search
Extract
Local
Scene Map
Match Results
Semantic
Tree
Clutter
Database
Form Associations
Refine Pose & Score
Analyze Mismatch
Tree
Clutter
Shadow
Obscuration ?
x1,y1, 
x2,y2, 
Score = 0.75
Feature-to-Model
Traceback
10
Match
Ground
Clutter
The e-Business / (CIM, CIE)
Order Processing
Customer Service
Sales Management
Process Coordination
Management &
Monitoring
Manufacturing
Product DBs
Inventory Shipping
Enterprise Messaging
Business
to
Customer
Web
e-commerce
11
Business
to
Business
Mobile Workers
Knowledge Workers
Business Communications
Distributor
Channel
Compare with
Classical (Old) Supply Chain
Parts
Supplier
Parts
Supplier
Manufacturing
Distribution
Retail
Customer
Customer
Manufacturing
Distribution
Retail
Customer
Customer
Manufacturing
Distribution
Retail
Customer
Customer
Transportation Supplier
12
Some Technology Challenges in
Enabling DDDAS
• Application development
– interfaces of applications with measurement systems
– dynamically select appropriate application components
– ability to switch to different algorithms/components
depending on streamed data
• Algorithms
– tolerant to perturbations of dynamic input data
– handling data uncertainties
• Systems supporting such dynamic environments
– dynamic execution support on heterogeneous
environments
– Extended Spectrum of platforms: assemblies of Sensor
Networks and Computational Grid platforms
– 13GRID Computing, and Beyond!!!
What is Grid Computing?
coordinated problem solving
on dynamic and heterogeneous resource assemblies
DATA
ACQUISITION
ADVANCED
VISUALIZATION
,ANALYSIS
QuickTime™ and a
decompressor
are needed to see this picture.
COMPUTATIONAL
RESOURCES
IMAGING INSTRUMENTS
LARGE-SCALE DATABASES
Example: “Telescience Grid”, Courtesy of Ellisman & Berman /UCSD&NPACI
14
The NGS Program developsTechnology for integrated feedback & control
Runtime Compiling System (RCS) and Dynamic Application Composition
Application
Model
Dynamic Analysis
Situation
Distributed
Programming
Model
Application
Program
Compiler
Front-End
Application
Intermediate
Representation
Compiler
Back-End
Launch
Application (s)
Dynamically
Link
&
Execute
Performance
Measuremetns
&
Models
Application
Components
&
Frameworks
Distributed Computing Resources
Distributed Platform
tac-com
alg accelerator
….
15
MPP
NOW
fire
cntl
data
base
data
base
fire
cntl
SAR
SP
Some more Challenges on
Applications Development Issues
• Handling Data Streams in addition to Data Sets
• Handling different data structures – semantic
information
• Interfaces to Measurement Systems
- Interactive Visualization and Steering
• Standards for data exchange
• Combining Local and Global Knowledge
• Model Interactions
• Application control of measurement systems
• Dynamic Application Composition and Runtime
support
(Examples from ITR supported efforts)
16
Important Point:
DDDAS is not just DATA ASSIMILATION!!!
• Data Assimilation compares/corrects specific
calculated points with experiments, rather than
dynamically as need
• Data Assimilation does not include the notion of
the simulation/application controlling the
measurement process
Rather…
Data Assimilation techniques can be used in certain
DDDAS cases
17
Programming Environments
• Procedural - > Model Based
• Programming -> Composition
• Custom Structures -> Customizable Structures
(patterns, templates)
• Libraries -> Frameworks ->
Compositional Systems
(Knowledge Based Systems)
• Application Composition Frameworks
and….
• Interoperability extended to include measurements
• Data Models and Data Management
– Extend the notion of Data Exchange Standards
(Applications and Measurements)
18
Additional Considerations/Requirements
on Hardware and Software Systems
• Extended Spectrum of platforms
– Assemblies of Computational Grid and Sensor Networks
platforms
• Systems Architectures including Measurement
Systems
• Programming Environments
• Application, System, and Resource Management
• Models of the Computational Infrastructure
• Security and Fault Tolerance
• DDDAS will accentuate and create the need for
advances in such areas
19
20
Impact to CyberInfrastructure
• The CyberInfrastructure that will result when
thinks of the present paradigm of (disjoint)
simulations and measurements will be different
than the CyberInfrastructure needed to support
DDDAS
• For example, bandwidth requirements, resource
allocation and other middleware and systems
software policies, prioritization, security, fault
tolerance, recovery, QoS, etc…, will be different
when one needs to guarantee data streaming to an
executing simulation or control of measurement
process
21
Why Now is the Time for DDDAS
• Technological progress has prompted advances in
some of the challenges
– Computing speeds advances (uni- and multi-processor
systems), Grid Computing, Sensor Networks
– Systems Software
– Applications Advances (parallel & grid computing)
– Algorithms advances (parallel &grid computing,
numeric and non-numeric techniques: dynamic meshing,
data assimilation)
• Examples of efforts in:
– Systems Software
– Applications
– 22Algorithms
Agency Efforts
NSF
– NGS: The Next Generation Software Program (1998- )
• develops systems software supporting dynamic resource execution
– Scalable Enterprise Systems Program (1999, 2000-2003)
• geared towards “commercial” applications
(Chaturvedi example)
– ITR: Information Technology Research (NSF-wide, FY00-04)
• has been used as an opportunity to support DDDAS related efforts
• In FY00 1 NGS/DDDAS proposal received; deemed best, funded
• In FY01, 46 ~DDDAS pre-proposals received; many meritorious;
24 proposals received; 8 were awarded
• In FY02, 31 ~DDDAS proposals received; 8(10) awards
• In FY02, so far: received 35 (“Small” ITR) proposals ~DDDAS;
more expected in the “Medium ITR” category -
– Gearing towards a DDDAS program
• expect participation from other NSF Directorates
• Looking for participation from other agencies!
23
“~DDDAS” proposals awarded
in FY00 ITR Competition
• Pingali, Adaptive Software for Field-Driven Simulations
24
“~DDDAS” proposals awarded
in FY01 ITR Competition
• Biegler – Real-Time Optimization for Data Assimilation and Control
of Large Scale Dynamic Simulations
• Car – Novel Scalable Simulation Techniques for Chemistry, Materials
Science and Biology
• Knight – Data Driven design Optimization in Engineering Using
Concurrent Integrated Experiment and Simulation
• Lonsdale – The Low Frequency Array (LOFAR) – A Digital Radio
Telescope
• McLaughlin – An Ensemble Approach for Data Assimilation in the
Earth Sciences
• Patrikalakis – Poseidon – Rapid Real-Time Interdisciplinary Ocean
Forecasting: Adaptive Sampling and Adaptive Modeling in a
Distributed Environment
• Pierrehumbert- Flexible Environments for Grand-Challenge Climate
Simulation
• Wheeler- Data Intense Challenge: The Instrumented Oil Field of
the Future
25
“~DDDAS” proposals awarded
in FY02 ITR Competition
• Carmichael – Development of a general Computational Framework for
the Optimal Integration of Atmospheric Chemical Transport Models
and Measurements Using Adjoints
• Douglas-Ewing-Johnson – Predictive Contaminant Tracking Using
Dynamic Data Driven Application Simulation (DDDAS) Techniques
• Evans – A Framework for Environment-Aware Massively Distributed
Computing
• Farhat – A Data Driven Environment for Multi-physics Applications
• Guibas – Representations and Algorithms for Deformable Objects
• Karniadakis – Generalized Polynomial Chaos: Parallel Algorithms for
Modeling and Propagating Uncertainty in Physical and Biological
Systems
• Oden – Computational Infrastructure for Reliable Computer
Simulations
• Trafalis – A Real Time Mining of Integrated Weather Data
26
Measured Response
A Homeland Security Simulation
(Briefed WH 5/14/02)
Alok Chaturvedi, Director
Shailendra Mehta, co-Director
Purdue e-Business Research Center
Partners
• Institute for Defense Analyses
• Office of VP IT, Purdue University
• Research and Academic
Computing, Indiana University
• Simulex, Inc
Parallel Worlds
Time
Compression
Simulation Loop
Decision Support Loop
Near exact replica
of the “real” world
Synthetic
Environment
Behavior
modeling,
demographics,
and calibration
SEAS architecture
Supports millions of
Artificial agents
Explore, Experiment, Learn,
Analyze, Test, & Anticipate
Real World
Environment
SCM
ERP
CRM
Data
Warehouse
Data collection,
association,
trends, and parameter
estimation
Implement, Assess
The user(s) can seamlessly
switch between real and
virtual worlds through an
intuitive user interface.
28
Reproduction Model
Get in contact
with infected
Susceptible
Exposed
Infected
w/o Symptoms
Immunized
Uninfected
entering incubation period
end of
incubation period
Immune
Infected
w/ Symptoms
Mortality
Succumb to the disease
Interventions:
Screen, Isolate (camp or shelter), Treat, Vaccinate
29
Mobility Models
•
•
•
•
•
30
Regular Movement
Event Traffic
Morning and Evening Rush
Evacuation
Panic Fleeing
New Infections
400
600
No Intervention
350
T6 Intervention
500
300
400
250
200
300
150
200
100
100
50
0
0
1
2
3
4
5
6
Local
State
7
8
9
10
1
11
2
3
4
5
6
Local State
Federal
250
7
8
9
10
11
Federal
120
T2 Intervention
T4 Intervention
100
200
80
150
60
100
40
50
20
0
0
1
2
31
3
4
5
Time 4 Intervention Local
6
Time 4 Intervention State
7
8
Time 4 Intervention Federal
9
10
11
1
2
3
4
5
Series1
6
Series2
Series3
7
8
9
10
Towards a National Grid for HLS
Data Fusion
Bio sensor
human
MEMS
electronic
Nano
Sensor Real World
32
The virtual world
NSF ITR Project
A Data Intense Challenge:
The Instrumented Oilfield of the Future
PI: Prof. Mary Wheeler, UT Austin
Multi-Institutional/Multi-Researcher Collaboration
33
Slide Courtesy of Wheeler/UTAustin
Highlights of Instrumented Oilfield Proposal
III. IT Technologies:
Data management, data visualization, parallel
computing, and decision-making tools such as
new wave propagation and multiphase, multicomponent flow and transport computational
portals, reservoir production:
THE INSTRUMENTED OILFIELD
IV. Major Outcome of Research:
Computing portals which will enable reservoir
simulation and geophysical calculations to
interact dynamically with the data and with each
other and which will provide a variety of visual
and quantitative tools. Test data provided by oil
and service companies
34
Economic Modeling and Well Management
Production Forecasting
Well Management
Reservoir
Performance
Data
Analysis
Simulation Models
Multiple Realizations
Data Management and Manipulation
Data Collections from Simulations and
Field Measurements
35
Visualization
Field
Measurements
Reservoir Monitoring
Field Implementation
ITR Project
A Data Intense Challenge:
The Instrumented Oilfield of the Future
II.
36
Industrial Support (Data):
i.
British Petroleum (BP)
ii.
Chevron
iii. International Business Machines (IBM)
iv. Landmark
v.
Shell
vi. Schlumberger
Dynamic Contrast Imaging
DCE-MRI (Osteosarcoma)
37
Dynamic Contrast Enhanced
Imaging
• Dynamic image quantification techniques
– Use combination of static and dynamic image
information to determine anatomic microstructure and
to characterize physiological behavior
– Fit pharmacokinetic models (reaction-convectiondiffusion equations)
– Collaboration with Michael Knopp, MD
38
Dynamic Contrast Enhanced
Imaging
• Dynamic image registration
– Correct for patient tissue motion during study
– Register anatomic structures between studies
and over time
• Normalization
– Images acquired with different patterns
spatio-temporal resolutions
– Images acquired using different imaging
modalities (e.g. MR, CT, PET)
39
Clinical Studies using Dynamic
Contrast Imaging
• 1000s of dynamic images per research study
• Iterative investigation of image
quantification, image registration and image
normalization techniques
• Assess techniques’ ability to correctly
characterize anatomy and pathophysiology
• “Ground truth” assessed by
– Biopsy results
– Changes in tumor structure and activity over
time with treatment
40
prior to therapy
1370
1370
after 2 cycles
1421
1421
1438
1438
after 4 cycles
Knopp
41 M,
OSU Radiology / dkfz
Software Support
• Component Framework for Combined
Task/Data Parallelism
– Use defines sequence of pipelined components -“filter group”
– User directive tells preprocessor/runtime system
to generate and instantiate copies of filters
– Many filter groups can be simultaneously active
– Integration proceeding with Globus/Network
Weather Service
42
Virtual Microscope
43
Adaptive Software Project
•Cornell University
–CS department (Keshav Pingali)
–Civil and Environmental Engineering (Tony Ingraffea)
•Mississippi State University
•University of Alabama, Birmingham
–Mechanical and Aerospace (Bharat Soni)
•College of William and Mary
•Ohio State University
•Clark-Atlanta University
44
SCOPE of ASP
Cracks: They’re Everywhere!
• Implement a system for multi-physics
multi-scale adaptive CSE simulations
– computational fracture mechanics
– chemically-reacting flow simulation
• Understand principles of
implementing adaptive software
systems
45
ASP Test Problem
46
Problem description
• Regenerative cooling nozzle from NASA
– Simplified geometry
• Chemically-reacting flow in interior of pipe
• Nozzle is cooled by fluid-flow in eight
smaller channels at periphery of pipe
• Problem:
–
–
–
–
47
simulate flows
determine crack growth
couple the multi-physics models
When successful add the ability to inject
monitoring measurements
Understanding fracture
• Wide range of length and time scales
• Macro-scale (1in- )
– components used in engineering practice
• Meso-scale (1-1000 microns)
– poly-crystals
• Micro-scale (1-1000 Angstroms)
– collections of atoms
10-3
48
10-6
10-9
m
Chemically-reacting flows
• MSU/UAB expertise in chemically-reacting
flows
• LOCI: system for automatic synthesis of
multi-disciplinary simulations
49
Pipe Workflow
MiniCAD
Modelt
Mechanical
Dispst
Viz
50
Surface
Mesher
Tst/Pst
Surface
Mesht
Fluid/Thermo
Generalized
Mesher
Fluid
Mesht
Client:
Crack
Initiation
Initial Flaw
Params
Crack
Insertion
Fracture
Mechanics
Growth
Params1
Crack
Extension
JMesh
T4 Solid
Mesht
T4T10
T10 Solid
Mesht
Modelt+1
Poseidon
Rapid Real-Time
Interdisciplinary Ocean Forecasting:
Adaptive Sampling and Adaptive Modeling
in a Distributed Environment
Nicholas M. Patrikalakis, Henrik Schmidt, MIT
Allan R. Robinson, James J. McCarthy,
Harvard
http://czms.mit.edu/poseidon
51
Ocean Science Issues
• Data driven simulations via data assimilation
• Simulation driven adaptive sampling of the
ocean
• Interdisciplinary ocean science: interactions
of physical, biological, acoustical phenomena
• Extend state-of-the-art via feedback from
acoustics to physical&biological oceanography
• Application in fisheries management, but also
in oil-slick containment
52
Interdisciplinary Ocean Science
53
Development of
a General Computational Framework
for the Optimal Integration
of Atmospheric Chemical Transport Models and
Measurements Using Adjoints
Greg Carmichael (Dept. of Chem. Eng., U. Iowa)
Adrian Sandu (Dept. of Comp. Sci., Mich. Inst. Tech.)
John Seinfeld (Dept. Chem. Eng., Cal. Tech.)
Tad Anderson (Dept. Atmos. Sci., U. Washington)
Peter Hess (Atmos. Chem., NCAR)
Dacian Daescu (Inst. of Appl. Math., U. Minn.)
54
Application: The Design of Better Observation Strategies to Improve
Chemical Forecasting Capabilities.
Example flight path of the NCAR C-130 flown to intercept a dust storm in East Asia that was
forecasted using chemical models as part of the NSF Ace-Asia (Aerosol Characterization Experiment
in Asia) Field Experiment
Will help to Better Determine Where and When to Fly and How to More Effectively
Deploy our Resources (People, Platforms, $s)
Shown are measured CO along the aircraft flight path, the brown isosurface represents modeled dust
(100 ug/m3), and the blue isosurface is CO (150 ppb) shaded by the fraction due to biomass burning
(green is more than 50%).
55
Project Goal:
To develop
general computational tools, and associated
software,
for assimilation of atmospheric chemical and
optical measurements into chemical transport
models (CTMs).
These tools are to be developed so that users
need not be experts in adjoint modeling and
optimization theory.
56
Approach:
•Develop novel and efficient algorithms for 4D-data
assimilation in CTMs;
Develop general software support tools to facilitate the
construction of discrete adjoints to be used in any
CTM;
•Apply these techniques to important applications
including:
(a) analysis of emission control strategies for Los
Angeles;
(b) the integration of measurements and models to
produce a consistent/optimal analysis data set for the
AceAsia intensive field experiment;
(c) the inverse analysis to produce a better estimate of
emissions; and
(d) the design of observation strategies to improve
chemical forecasting capabilities.
57
Data Assimilation for Chemical Models
Solid lines represent current capabilities
Dotted lines represent new analysis capabilities
Future: enable DDDAS capabilities
58
General Software Tools Framework
to
Facilitate the Close Integration of Measurements and Models
The framework will provide tools for: 1) construction of the adjoint model; 2)
handling large datasets; 3) checkpointing support; 4) optimization; 5) analysis of
results; 6) remote access to data and computational resources.
59
Modeling Uncertainty
Irreducible versus epistemic uncertainty
•
•
•
•
•
•
Stochastically-excited structures
Boundary conditions, geometry, properties
Sensitivity/failure analysis
Gaussian and non-Gaussian processes
Polynomial Chaos vs. Monte Carlo
Stochastic spectral/hp element methods
“…Because I had worked in the closest possible ways with
physicists and engineers, I knew that our data can never be precise…”
Norbert Wiener
Slides Courtesy of Karniadakis/Brown
60
Partially Correlated non-Uniform Random Inflow
•Deterministic
•Stochastic
•Pressure
Vorticity: Regions of Uncertainty
61
Non-uniform Gaussian Random BC
• Exponential correlation
C( x 1 , x 2 )   2 e  x  x
1
• Stochastic input:   0.1
Umean along centerline
62
• 2D K-L expansion
2
/b
• 4th-order Hermite-Chaos expansion
• 15-term expansion
Vmean along centerline
Non-uniform Exponential Random BC
• Exponential correlation
C( x 1 , x 2 )   2 e  x  x
1
• Stochastic input:   0.1
Umean along centerline
63
• 2D K-L expansion
2
/b
• 4th-order Laguerre-Chaos expansion
• 15-term expansion
Vmean along centerline
Research Opportunities in Uncertainty
UUncertainty
analysis is a fertile and much needed area
for inter-disciplinary research
EEstimates of uncertainties in model inputs are
desperately needed
Uncertainty  Ignorance
64
What about Industry &DDDAS
• Industry has history of
– forging new research and technology directions and
– adapting and productizing technology which has demonstrated promise
• Need to strengthen the joint academe/industry
research collaborations; joint projects / early stages
• Technology transfer
– establish path for tech transfer from academic research to industry
– joint projects, students, sabbaticals (academe <----> industry)
• Initiatives from the Federal Agencies / PITAC
• Cross-agency co-ordination
• Effort analogous to VLSI, Networking, and
Parallel and Scalable computing
• Industry is interested in DDDAS
65
Research and Technology Roadmap
(emphasis on multidisciplinary research)
Application Composition System
}
•Distributed programming models
•Application performance Interfaces
•Compilers optimizing mappings on complex
systems
..
.
}
Application RunTime System
•Automatic selection of solution methods
•Interfaces, data representation & exchange
•Debugging tools
..
.
Measurement System
}
•Application/system multi-resolution models
•Modeling languages
•Measurement and instrumentation
..
.
Y1
i
n
t
e
g
r
a
t
i
o
n
Y2
Exploratory
i
n
t
D E
Eg
r
a
t
i
o
n
Y3
Y4
M
O
Y5
Development
Integration & Demos
66
S
Providing
enhanced
capabilities
for
Applications
DDDAS has potential
for significant impact to
science, engineering, and commercial world,
akin to the transformation effected
since the ‘50s
by the advent of computers
DDDAS:
http://www.cise.nsf.gov/dddas
http://www.dddas.org
NGS:
http://www.cise.nsf.gov/div/acir
67
Following is a
List of Presentations of DDDAS projects
at the
International Conference on Computational Sciences
June 2-6, 2003, Melbourne Australia
68
Dynamic Data Driven Application Systems
WORKSHOP (June 2 & June 3)
Agenda (Titles of presentations and speakers)
Mon June 2
Session 1 (3:30pm- 4:15pm)
 Introduction: Dynamic Data Driven Application System
Frederica Darema, NSF
 Guest Talk: Bayesian Methods for Dynamic Data Assimilation and
Process Design in the Presence of Uncertainties
Greg McRae, MIT
Session 2 (4:30pm- 6:00pm)
 Computational Science Simulations based on Web Services
Keshav Pingali, Cornell U.
 Driving Scientific Applications by Data in Distributed Environments
Joel Saltz, The Ohio State University
 DDEMA: A Data Driven Environment for Multiphysics Applications
John Michopoulos, NRL
69
Dynamic Data Driven Application Systems
WORKSHOP
Tues June 3
Session 3 (9:30am- 10:30am)
 Computational Aspects of Chemical Data Assimilation into
Atmospheric Models
Gregory Carmichael, U of Iowa
 Virtual Telemetry for Dynamic Data-Driven Application Simulations
Craig C. Douglas, University of Kentucky and Yale University
Session 4 (11:00am- 12:30pm)
 Tornado Detection with Support Vector Machines
Theodore B. Trafalis, University of Oklahoma
 A Computational Infrastructure for Reliable Computer Simulations
Jim Browne, UTAustin
 Discrete Event Solution of gas Dynamics within the DEVS
Framework: Exploiting Spatiotemporal Heterogeneity
James Nutaro – U of Arizona
70
Dynamic Data Driven Application Systems
WORKSHOP
Tues June 3 (cont’d)
Session 5 (2:30pm- 3:30pm)
 Data Driven Design Optimization Methology: A Dynamic Data Driven
Application System
Doyle Knight, Rutgers U.
 Rapid Real-Time Interdisciplinary Ocean Forecasting Using Adaptive
Sampling and Adaptive Modeling and legacy Codes: Component
Ecapsulation using XML
Constantinos Evangelinos, MIT
Session 6 (4:00am- 5:30pm)
 Generalized Polynomial Chaos: Algorithms for Modeling and
Propagation of Uncertainty
Dongbin Xiu, Brown University
 Derivation of Natural Stimulus Feature Set Using A Data Driven
Model
John Miller, Montana State U.
 Simulating Seller’s Behavior in a Reverse Auction B2B Exchange
Alok Chaturvedi, Purdue U.
71