1

Dr. Frederica DaremaSenior Science and Technology Advisor

Director, Next Generation Software Program

NSF

Dynamic Data Driven Application Systems(DDDAS)

A new paradigm for applications/simulations

andmeasurement methodology

… and how it would impact CyberInfrastructure!

2

Measurements ExperimentField-Data

User

Theory

(First P

rincip

les)

Simula

tions

(Math

.Modeli

ng

Phenomenol

ogy)Experiment

MeasurementsField-Data

(on-line/archival)User

Theory

(First P

rincip

les)

Simula

tions

(Math

.Modelin

g

Phenomenolo

gy

Observ

ation M

odeling

Design)

OLD

(serialized and static)

NEW PARADIGM

(Dynamic Data-Driven Simulation Systems)

Challenges:Application Simulations DevelopmentAlgorithms Computing Systems Support

Dynam

ic

Feed

back

& C

ontro

l

Loop

What is DDDAS(Symbiotic Measurement&Simulation Systems)

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 modelingJanice 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 SimulationsDick 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

PETROLEUM APPLICATIONS PETROLEUM APPLICATIONS

GAS

OIL

WATERFAULT

SALT

DOME

6

Surface hydrophone array

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. photoSlide Courtesy of Coen/NCAR

8

Coupled atmospheric and wildfire models

Slide Courtesy of Coen/NCAR

9

AMAT Centura Chemical Vapor Deposition ReactorAMAT Centura Chemical Vapor Deposition Reactor

Operating ConditionsReactor Pressure 1 atmInlet Gas Temperature 698 KSurface Temperature 1173 KInlet Gas-Phase Velocity 46.6 cm/sec

SiCl3H HCl + SiCl2

SiCl2H2 SiCl2 + H2 SiCl2H2 HSiCl + HClH2ClSiSiCl3 SiCl4 + SiH2

H2ClSiSiCl3 SiCl3H + HSiClH2ClSiSiCl3 SiCl2H2 + SiCl2

Si2Cl5H SiCl4 + HSiClSi2Cl5H SiCl3H + SiCl2

Si2Cl6 SiCl4 + SiCl2

Gas Phase ReactionsGas Phase ReactionsSiCl3H + 4s Si(B) + sH + 3sClSiCl2H2 + 4s Si(B) + 2sH + 2sClSiCl4 + 4s Si(B) + 4sClHSiCl + 2s Si(B) + sH + sClSiCl2 + 2s Si(B) + 2sCl2sCl + Si(B) SiCl2 + 2sH2 + 2s 2sH2sH 2s + H2

HCl + 2s sH + sClsH + sCl 2s + HCl

Surface ReactionsSurface Reactions

Slide Courtesy of McRae/MIT

10

MSTAR (DARPA)(Moving and Stationary Target Acquisition and Recognition)

Predict Extract

Focus ofAttention

SAR Image &Collateral Data- DTED, DFAD- Site Models- EOSAT imagery

ROAD

TREES

GRASS

H2O

Regions of Interest (ROI)

SegmentedTerrain Map

Indexing

...Search Tree

...Index Database(created off-line)

Search

Target & Scene Model Database(created off line)

Match

LocalScene Map

GRASS

TREES

TR

EE

S

ROI Hypothesis

x

y BMP-2

Shadow (?)

Match Results

Score = 0.75

Form Associations Refine Pose & Score

x1,y1, x2,y2,

Analyze Mismatch

TreeClutter

GroundClutter

ShadowObscuration ?

Feature-to-Model Traceback

Task Predict Task Extract

LocalScene Map

GRASS

TREES

TR

EE

S

ROI Hypothesis

x

y BMP-

2

Target& ClutterDatabase

StatisticalModel

CAD

Semantic Tree

ClutterDatabase

11

Application

Integration

Interoperability

The e-Business / (CIM, CIE)

Enterprise Messaging

ManufacturingProduct DBsInventory Shipping

Order Processing Customer ServiceSales Management

Process Coordination Management & Monitoring

Data

Integration

Interoperability

DistributorChannel

Businessto

Business

Webe-commerce

Businessto

Customer

Mobile WorkersKnowledge WorkersBusiness Communications

12

Compare with Classical (Old) Supply Chain

PartsSupplier

Manufacturing Distribution RetailCustomer

Customer

Manufacturing Distribution RetailCustomer

Customer

Manufacturing Distribution RetailCustomer

Customer

PartsSupplier

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

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

IMAGING INSTRUMENTS

COMPUTATIONALRESOURCES

LARGE-SCALE DATABASES

DATA ACQUISITION ,ANALYSIS

ADVANCEDVISUALIZATION

Example: “Telescience Grid”, Courtesy of Ellisman & Berman /UCSD&NPACI

15

DynamicallyLink

&Execute

The NGS Program developsTechnology for integrated feedback & control Runtime Compiling System (RCS) and Dynamic Application

CompositionApplication

Model

Application Program

ApplicationIntermediate

Representation

CompilerFront-End

CompilerBack-End Performance

Measuremetns&

Models

DistributedProgramming

Model

ApplicationComponents

&Frameworks

Dynamic AnalysisSituation

LaunchApplication (s)

Distributed Platform

Ada

ptab

leco

mpu

ting

Syst

ems

Infr

astr

uctu

re

Distributed Computing Resources

MPP NOW

SAR

tac-com

database

firecntl

firecntl

alg accelerator

database

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

Perform

ance

Engineering

Dynamic

Compilers

&

Application

Composition

Dynamic Data-Driven

Applicatio

n Systems

--

Symbiotic

Measurement&Simulatio

n

Systems

TowardsEnabling DDDAS

NGS Program

Today’s Grid

Environments:

“Users

shouldn’t

Have to be Heroes

to Achieve Grid

Program Perform

ance”

and... beca

use heroism

is not e

nough

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

Measured ResponseMeasured ResponseA Homeland Security SimulationA Homeland Security Simulation

(Briefed WH 5/14/02)(Briefed WH 5/14/02)

Alok Chaturvedi, DirectorShailendra 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

Partners• Institute for Defense Analyses• Office of VP IT, Purdue University• Research and Academic

Computing, Indiana University• Simulex, Inc

28

Parallel Worlds

Real WorldEnvironment

Explore, Experiment, Learn, Analyze, Test, & Anticipate Implement, Assess

Behaviormodeling,

demographics,and calibration

Data collection,association,

trends, and parameterestimation

TimeCompression

Near exact replicaof the “real” world

SEAS architectureSupports millions ofArtificial agents

Decision Support Loop

SyntheticEnvironment

The user(s) can seamlessly switch between real and virtual worlds through an intuitive user interface.

SCMERPCRMData

Warehouse

Simulation Loop

29

Reproduction Model

Susceptible

Mortality

ExposedInfected

w/o Symptoms

Infected w/ Symptoms

Immune

recovered

Succumb to the disease

mor

tality

not d

ue to

infe

ction

entering incubation period

end ofincubation period

Interventions: Screen, Isolate (camp or shelter), Treat, Vaccinate

Get in contact with infected

Uninfected

Imm

unized

30

Mobility Models

• Regular Movement• Event Traffic• Morning and Evening Rush• Evacuation• Panic Fleeing

31

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10

Series1 Series2 Series3

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11

Time 4 Intervention Local Time 4 Intervention State Time 4 Intervention Federal

0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11

Local State Federal

0

100

200

300

400

500

600

1 2 3 4 5 6 7 8 9 10 11

Local State Federal

New Infections

No Intervention

T4 Intervention T2 Intervention

T6 Intervention

32

Towards a National Grid for HLS

Real World

The virtual world

Data Fusion

Bio sensor

NanoSensor

MEMS

electronic

human

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

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

THE INSTRUMENTED OILFIELD

III. 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:

35

Data Management and Manipulation

Visualization

Field Measurements

Simulation Models

Reservoir MonitoringField Implementation

Data Analysis

Production ForecastingWell Management

ReservoirPerformance

Data Collections from Simulations and Field Measurements

Economic Modeling and Well Management

Multiple Realizations

36

ITR Project

A Data Intense Challenge:

The Instrumented Oilfield of the Future

II. Industrial Support (Data):

i. British Petroleum (BP)

ii. Chevron

iii. International Business Machines (IBM)

iv. Landmark

v. Shell

vi. 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-diffusion equations)

– Collaboration with Michael Knopp, MD

39

• 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)

Dynamic Contrast Enhanced Imaging

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

1370

1421

1438

prior to therapy

after 2 cycles

after 4 cycles

1370

14211421

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

• 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

Cracks: They’re Everywhere!

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

10-3 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

Tst/Pst

SurfaceMesht

FluidMesht

T4 SolidMesht

Modelt

T10 SolidMeshtDispst

Initial FlawParams

SurfaceMesher

GeneralizedMesher

JMesh

T4T10

Fluid/ThermoMechanical

CrackInsertion

Client:CrackInitiation

FractureMechanics

CrackExtension

GrowthParams1

Modelt+1

MiniCAD

Viz

Poseidon

Rapid Real-TimeInterdisciplinary Ocean Forecasting:

Adaptive Sampling and Adaptive Modelingin a Distributed Environment

Nicholas M. Patrikalakis, Henrik Schmidt, MITAllan 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 physical&biological 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

• 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

Irreducible versus epistemic uncertainty

“…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

Vorticity: Regions of Uncertainty

•Pressure

•Deterministic

•Stochastic

62

Non-uniform Gaussian Random BC

Umean along centerline Vmean along centerline

• Exponential correlationb/xx2

2121e)x,x(C

1.0• Stochastic input:

• 2D K-L expansion

• 4th-order Hermite-Chaos expansion

• 15-term expansion

63

Non-uniform Exponential Random BC

Umean along centerline Vmean along centerline

• Exponential correlationb/xx2

2121e)x,x(C

1.0• Stochastic input:

• 2D K-L expansion

• 4th-order Laguerre-Chaos expansion

• 15-term expansion

64

Research Opportunities in UncertaintyResearch Opportunities in Uncertainty

UUncertainty analysis is a fertile and much needed area for inter-disciplinary researchEEstimates of uncertainties in model inputs are desperately needed

Uncertainty Uncertainty Ignorance 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

i

e

nt

gratio

n

Research and Technology Roadmap (emphasis on multidisciplinary research)

Y1 Y2 Y3 Y4 Y5Exploratory Development

Integration & Demos

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

Providing enhanced

capabilities for

Applications

DE

MOS

...

...

...

}

}

}

i

E

nt

gratio

n

67

DDDAS:http://www.cise.nsf.gov/dddashttp://www.dddas.orgNGS:http://www.cise.nsf.gov/div/acir

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

68

Following is aList 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 UncertaintiesGreg 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 HeterogeneityJames 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 XMLConstantinos 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 ModelJohn Miller, Montana State U.

Simulating Seller’s Behavior in a Reverse Auction B2B Exchange Alok Chaturvedi, Purdue U.