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

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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)

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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 & Environmental Systems– transportation systems (planning, accident response)– weather, hurricanes/tornadoes, floods, fire propagation

• Medical– Imaging, 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

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NSF Workshop on DDDAS

• 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

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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 Grids; measurement systems– GRID Computing, and Beyond!!!

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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

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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 (complex/multimodal/multiscale

modeling, 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

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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 FY03, 35 (“Small” ITR) & 34 (“medium” ITR) proposals ~DDDAS;

funded 2 small, 6 medium, 1 large

– Gearing towards a DDDAS program• expect participation from other NSF Directorates

• Looking for participation from other agencies!

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“~DDDAS” projects related to Med/Bio

Through ITR:Awarded in FY01

• Wheeler- Data Intense Challenge: The Instrumented Oil Field of the Future– Saltz (Ohio State)– Radiology Imagery – Virtual Microscope

Awarded in FY02• Douglas-Ewing-Johnson – Predictive Contaminant Tracking Using

Dynamic Data Driven Application Simulation (DDDAS) Techniques– Johnson (Utah) – Interactive Physiology Systems

• Guibas – Representations and Algorithms for Deformable Objects– Metaxas (Rutgers) – Medical Image Analysis – heart/lung modeling,

tumorsThrough NGS: • Microarray Experiment Management System

– Ramakirishnan (V.Tech)– PSE and Recommender SystemThrough BITS• Algorithms for RT Recording and Modulation of Neural Spike

Trains– Miller (U. Montana)

Examples of DDDAS efforts

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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

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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:

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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

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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

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Dynamic Contrast ImagingDCE-MRI (Osteosarcoma)

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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

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• 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

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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

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Virtual Microscope

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SCOPE of ASP (CornellU)

• 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!

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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

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Chemically-reacting flows

• MSU/UAB expertise in chemically-reacting flows

• LOCI: system for automatic synthesis of multi-disciplinary simulations

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ASP Test Problem: Pipe

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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

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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

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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

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http://www.cise.nsf.gov/dddas

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

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“~DDDAS” proposals awarded in FY00 ITR Competition

• Pingali, Adaptive Software for Field-Driven Simulations

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“~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

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“~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