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Dynamic Data Driven Application Systems

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Title: Dynamic Data Driven Application Systems


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
What is DDDAS
(Symbiotic MeasurementSimulation Systems)
Simulations (Math.Modeling Phenomenology Observati
on Modeling Design)
Theory (First Principles)
Simulations (Math.Modeling Phenomenology)
Theory (First Principles)
Experiment Measurements Field-Data (on-line/archiv
al) User
Measurements Experiment Field-Data User
Dynamic Feedback Control 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 Appls)
  • 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 AM 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

5
PETROLEUM APPLICATIONS
SALT DOME
GAS
OIL
WATER
FAULT
6
Surface hydrophone array
7
Fire Model
  • Sensible and latent heat fluxes from ground and
    canopy fire -gt heat fluxes in the atmospheric
    model.
  • Fires heat fluxes are absorbed by air over a
    specified extinction depth.
  • 56 fuel mass -gt 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
Slide Courtesy of McRae/MIT
10
MSTAR (DARPA)(Moving and Stationary Target
Acquisition and Recognition)
Focus of Attention
Index Database (created off-line)
...
Search Tree
Regions of Interest (ROI)
Segmented Terrain Map
SAR Image Collateral Data - DTED, DFAD - Site
Models - EOSAT imagery
...
Indexing
Target Scene Model Database (created off line)
Task Predict
Task Extract
Extract
Search
Statistical Model
Predict
Clutter Database
CAD
Match Results
Tree Clutter
Semantic Tree
Form Associations
Refine Pose Score
Analyze Mismatch
Shadow Obscuration ?
x2,y2, ??
x1,y1, ??
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 Business
Enterprise Messaging
Data Integration Interoperability
Mobile Workers Knowledge Workers Business
Communications
Business to Customer
Web e-commerce
12
Compare with Classical (Old) Supply Chain
Parts Supplier
Parts Supplier
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/componen
    ts 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
ADVANCEDVISUALIZATION
,ANALYSIS
COMPUTATIONALRESOURCES
IMAGING INSTRUMENTS
LARGE-SCALE DATABASES
Example Telescience Grid, Courtesy of Ellisman
Berman /UCSDNPACI
15
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)
Performance Measuremetns Models
Dynamically Link Execute
Application Components Frameworks
Distributed Computing Resources
Distributed Platform
Adaptable computing Systems Infrastructure
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 - gt Model Based
  • Programming -gt Composition
  • Custom Structures -gt Customizable Structures
  • (patterns, templates)
  • Libraries -gt Frameworks -gt
  • 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
Todays Grid Environments Users shouldnt Have
to be Heroes to Achieve Grid Program
Performance and... because heroism is not enough
Dynamic Data-Driven Application
Systems -- Symbiotic MeasurementSimulation Syste
ms
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
  • Systems Software
  • 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,
    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!

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 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
Parallel Worlds
29
Reproduction Model
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
T4 Intervention
32
Towards a National Grid for HLS
Data Fusion
Bio sensor
human
MEMS
The virtual world
electronic
Nano Sensor
Real World
33
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
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 ImagingDCE-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-di
    ffusion 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
prior to therapy
1370
1370
after 2 cycles
1421
1421
1421
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
SCOPE of ASP
Cracks Theyre 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 (1-1000 microns)
  • poly-crystals
  • Micro-scale (1-1000 Angstroms)
  • collections of atoms

10-6
m
10-9
10-3
49
Chemically-reacting flows
  • MSU/UAB expertise in chemically-reacting flows
  • LOCI system for automatic synthesis of
    multi-disciplinary simulations

50
Pipe Workflow
MiniCAD
SurfaceMesht
SurfaceMesher
GeneralizedMesher
JMesh
Modelt
T4 SolidMesht
FluidMesht
Tst/Pst
Fluid/Thermo
Mechanical
T4?T10
T10 SolidMesht
Client CrackInitiation
Initial FlawParams
CrackInsertion
Dispst
Modelt1
FractureMechanics
CrackExtension
GrowthParams1
Viz
51
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

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 physicalbiological oceanography
  • Application in fisheries management, but also in
    oil-slick containment

53
Interdisciplinary Ocean Science
54
Development ofa 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
ExperimentWill 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
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

62
Non-uniform Gaussian Random BC
Umean along centerline
Vmean along centerline
63
Non-uniform Exponential Random BC
Umean along centerline
Vmean along centerline
64
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
    lt----gt 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
Research and Technology Roadmap (emphasis on
multidisciplinary research)
Application Composition System


Distributed programming models
.

Application performance Interfaces
.
.
i

Compilers optimizing mappings on complex
systems
n
t
D
Providing

Application RunTime System
E
E
enhanced
g

Automatic selection of solution methods
.
.

Interfaces, data representation exchange
M
capabilities
.

Debugging tools
O
r
for
S
Applications
a
t
Measurement System

i
o
.

Application/system multi-resolution models
.

Modeling languages
.

Measurement and instrumentation
n
Y1
Y2
Y3
Y4 Y5
Exploratory
Development
Integration Demos
67
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.dd
das.org NGS http//www.cise.nsf.gov/div/acir
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 (330pm- 415pm)
  •  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 (430pm- 600pm)
  • 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 (930am- 1030am)
  • 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 (1100am- 1230pm)
  • 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 (contd)
  • Session 5 (230pm- 330pm)
  • 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 (400am- 530pm)
  • 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 Sellers Behavior in a Reverse Auction
    B2B Exchange
  • Alok Chaturvedi, Purdue U.
  •  
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