1. 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
NSF

2. (Dynamic Data-Driven Simulation Systems) (serialized and static)
What is DDDAS
(Symbiotic Measurement&Simulation Systems)
(Dynamic Data-Driven Simulation Systems)
NEW PARADIGM
Simulations
(Math.Modeling
Phenomenology
Observation Modeling
Design)
(serialized and static)
OLD
(First Principles)
Theory
Simulations
(Math.Modeling
Phenomenology)
(First Principles)
Theory
Experiment
Measurements
Field-Data
(on-line/archival)
User
Measurements
Experiment
Field-Data
User
Feedback & Control
Dynamic
Loop
Challenges:
Application Simulations Development
Algorithms
Computing Systems Support

3. 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

4. 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:

5. PETROLEUM APPLICATIONS
SALT
DOME
GAS
OIL
WATER
FAULT

6. Surface hydrophone array
Source: Schlumberger online oilfield glossary at

7. Fire Model
Sensible and latent heat fluxes from ground and canopy fire -> heat fluxes in the atmospheric model.
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.
Ground heat flux used to dry and ignite the canopy.
Kirk Complex Fire. U.S.F.S. photo
Slide Courtesy of Coen/NCAR

8. Coupled atmospheric and wildfire models
Slide Courtesy of Coen/NCAR

9. AMAT Centura Chemical Vapor Deposition Reactor
Operating Conditions
Reactor Pressure 1 atm
Inlet Gas Temperature 698 K
Surface Temperature 1173 K
Inlet Gas-Phase Velocity 46.6 cm/sec
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
Gas Phase Reactions
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
Surface Reactions
Slide Courtesy of McRae/MIT

10. MSTAR (DARPA) (Moving and Stationary Target Acquisition and Recognition)
ROAD
TREES
GRASS
H2O
Focus of
Attention
Index Database
(created off-line)
Search Tree
...
Target
& Clutter
Database
Regions of
Interest (ROI)
Segmented
Terrain Map
SAR Image &
Collateral Data
- DTED, DFAD
- Site Models
- EOSAT imagery
...
Local
Scene Map
GRASS
TREES
ROI Hypothesis x y 
BMP-2
Indexing
Target & Scene
Model Database
(created off line)
Task Predict
Task Extract
Local
Scene Map
GRASS
TREES
ROI Hypothesis x y 
BMP-2
Shadow
(?)
Statistical
Model
Predict
Search
Extract
Clutter
Database
CAD
Match Results
Tree
Clutter
Semantic
Tree
Form Associations
Refine Pose & Score
Analyze Mismatch
Shadow
Obscuration ?
x1,y1, 
x2,y2, 
Score = 0.75
Ground
Clutter
Feature-to-Model
Traceback
Match

11. The e-Business / (CIM, CIE)
Distributor
Channel
Order Processing
Customer Service
Sales Management
Manufacturing
Product DBs
Inventory Shipping
Application
Integration
Interoperability
Process Coordination
Management &
Monitoring
Business to
Enterprise Messaging
Data
Integration
Interoperability
Mobile Workers
Knowledge Workers
Business Communications
Business to
Customer
Web
e-commerce

12. Compare with Classical (Old) Supply Chain
Manufacturing
Distribution
Retail
Customer
Parts
Supplier
Manufacturing
Distribution
Retail
Customer
Parts
Supplier
Manufacturing
Distribution
Retail
Customer
Transportation Supplier

13. 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
GRID Computing, and Beyond!!!

14. What is Grid Computing? coordinated problem solving
on dynamic and heterogeneous resource assemblies
DATA ACQUISITION
ADVANCED VISUALIZATION
,ANALYSIS
COMPUTATIONAL RESOURCES
IMAGING INSTRUMENTS
LARGE-SCALE DATABASES
Example: “Telescience Grid”, Courtesy of Ellisman & Berman /UCSD&NPACI

15. The NGS Program developsTechnology for integrated feedback & control Runtime Compiling System (RCS) and Dynamic Application Composition
Dynamic Analysis
Situation
Application
Model
Distributed
Programming
Model
Application
Program
Compiler
Front-End
Application
Intermediate Representation
Compiler
Back-End
Launch
Application (s)
Performance
Measuremetns
&
Models
Dynamically
Link
&
Execute
Application
Components
&
Frameworks
Distributed Computing Resources
Distributed Platform
computing Systems
Adaptable
Infrastructure
MPP
NOW
SAR
tac-com
base
data
cntl
fire
alg accelerator SP ….

16. 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)

17. 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

18. 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)

19. 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

20. Towards Enabling DDDAS
“Users shouldn’t Have to be Heroes
Today’s Grid Environments:
to Achieve Grid Program Performance”
and... because heroism is not enough
Dynamic Data-Driven
Application Systems
Measurement&Simulation
Symbiotic --
Systems
Dynamic
Compilers
Application
&
Composition
NGS Program
Performance
Engineering

21. 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

22. 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:
Applications
Algorithms

23. Agency Efforts NSF NGS: The Next Generation Software Program (1998- )
develops systems software supporting dynamic resource execution
Scalable Enterprise Systems Program (1999, )
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; 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!

24. “~DDDAS” proposals awarded in FY00 ITR Competition
Pingali, Adaptive Software for Field-Driven Simulations

25. “~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

26. “~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

27. Measured Response Partners 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

28. Explore, Experiment, Learn, Analyze, Test, & Anticipate
Parallel Worlds
Real World
Environment
Explore, Experiment, Learn, Analyze, Test, & Anticipate
Implement, Assess
Behavior
modeling,
demographics,
and calibration
Data collection,
association,
trends, and parameter
estimation
Time
Compression
Near exact replica
of the “real” world
SEAS architecture
Supports millions of
Artificial agents
Decision Support Loop
Synthetic
The user(s) can seamlessly switch between real and virtual worlds through an intuitive user interface.
SCM
ERP
CRM
Data
Warehouse
Simulation Loop

29. Reproduction Model Interventions:
Get in contact
with infected
Infected
w/o Symptoms
Susceptible
Exposed
entering incubation period
Uninfected
Immunized
end of
incubation period
mortality not due to infection
Immune
recovered
Infected
w/ Symptoms
Mortality
Succumb to the disease
Interventions:
Screen, Isolate (camp or shelter), Treat, Vaccinate

30. Mobility Models Regular Movement Event Traffic
Morning and Evening Rush
Evacuation
Panic Fleeing

31. New Infections T6 Intervention No Intervention T2 Intervention

32. Towards a National Grid for HLS
Data Fusion
Bio sensor
human
MEMS
The virtual world
Nano
Sensor
electronic
Real World

33. A Data Intense Challenge: The Instrumented Oilfield of the Future
NSF ITR Project
A Data Intense Challenge:
The Instrumented Oilfield of the Future
PI: Prof. Mary Wheeler, UT Austin
Multi-Institutional/Multi-Researcher Collaboration
Slide Courtesy of Wheeler/UTAustin

34. Highlights of Instrumented Oilfield Proposal
IT Technologies:
Data management, data visualization, parallel computing, and decision-making tools such as new wave propagation and multiphase, multi- component flow and transport computational portals, reservoir production:
THE INSTRUMENTED OILFIELD
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

35. Economic Modeling and Well Management
Production Forecasting
Well Management
Reservoir
Performance
Simulation Models
Visualization
Data
Analysis
Multiple Realizations
Field
Measurements
Data Management and Manipulation
Reservoir Monitoring
Field Implementation
Data Collections from Simulations and
Field Measurements

36. A Data Intense Challenge: The Instrumented Oilfield of the Future
ITR Project
A Data Intense Challenge:
The Instrumented Oilfield of the Future
Industrial Support (Data):
British Petroleum (BP)
Chevron
International Business Machines (IBM)
Landmark
Shell
Schlumberger

37. Dynamic Contrast Imaging DCE-MRI (Osteosarcoma)

38. 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-convection-diffusion equations)
Collaboration with Michael Knopp, MD

39. 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)

40. 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

41. Knopp M, OSU Radiology / dkfz
prior to therapy
1370
1370
after 2 cycles
1421
1421
1421
Diese Datei und alle darin enthaltenen Bilder sind Eigentum von Michael V. Knopp.
Jede Benutzung, Veröffentlichung oder Weitergabe gleich welcher Art ist nicht statthaft, wenn dies nicht ausdrücklich und schriftlich genehmigt wurde.
Fragen und Mitteilungen an
This is protected information. Any copies or distribution without the express written approval of Dr. M. Knopp is prohibited
after 4 cycles
1438
1438
Knopp M, OSU Radiology / dkfz

42. 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

43. Virtual Microscope

44. 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

45. Cracks: They’re Everywhere!
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

46. ASP Test Problem

47. 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:
simulate flows
determine crack growth
couple the multi-physics models
When successful add the ability to inject monitoring measurements

48. Understanding fracture
Wide range of length and time scales
Macro-scale (1in- )
components used in engineering practice
Meso-scale ( microns)
poly-crystals
Micro-scale ( Angstroms)
collections of atoms
10-3
10-6
10-9 m

49. Chemically-reacting flows
MSU/UAB expertise in chemically-reacting flows
LOCI: system for automatic synthesis of multi-disciplinary simulations

50. Pipe Workflow MiniCAD Surface Mesher Surface Mesht Generalized Mesher
Modelt
JMesh
T4 Solid Mesht
Fluid Mesht
Mechanical
Tst/Pst
Fluid/Thermo
T4T10
Client:
Crack Initiation
T10 Solid Mesht
Initial Flaw Params
Crack Insertion
Dispst
Modelt+1
Fracture Mechanics
Growth Params1
Crack Extension
Viz

51. Adaptive Sampling and Adaptive Modeling
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

52. 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

53. Interdisciplinary Ocean Science

54. 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.)

55. 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%).

56. 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.

57. 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.

58. Data Assimilation for Chemical Models
Solid lines represent current capabilities Dotted lines represent new analysis capabilities Future: enable DDDAS capabilities

59. 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.

60. Modeling Uncertainty Stochastically-excited structures
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

61. Partially Correlated non-Uniform Random Inflow
Deterministic
Stochastic
Pressure
Vorticity: Regions of Uncertainty

62. Non-uniform Gaussian Random BC
Exponential correlation
Stochastic input:
2D K-L expansion
4th-order Hermite-Chaos expansion
15-term expansion
Umean along centerline
Vmean along centerline

63. Non-uniform Exponential Random BC
Exponential correlation
Stochastic input:
2D K-L expansion
4th-order Laguerre-Chaos expansion
15-term expansion
Umean along centerline
Vmean along centerline

64. Uncertainty  Ignorance
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

65. 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

66. (emphasis on multidisciplinary research)
Research and Technology Roadmap
(emphasis on multidisciplinary research) i e n t g r a o
Application Composition System } •
Distributed programming models . •
Application performance Interfaces . •
Compilers optimizing mappings on complex . i
systems n t D
Application RunTime System } E
Providing E
enhanced •
Automatic selection of solution methods . g •
Interfaces, data representation & exchange . M .
capabilities •
Debugging tools r O
for S a
Applications t
Measurement System } i •
Application/system multi-resolution models . o •
Modeling languages . . •
Measurement and instrumentation n Y1 Y2 Y3
Y Y5
Exploratory
Development
Integration & Demos

67. DDDAS: http://www.cise.nsf.gov/dddas http://www.dddas.org NGS:
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:
NGS:

68. backup slides Following is a List of Presentations of DDDAS projects
at the
International Conference on Computational Sciences
June 2-6, 2003, Melbourne Australia

69. 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

70. 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

71. 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.