Dr. Frederica Darema

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Dynamic Data Driven Application
Systems (DDDAS) A new paradigm for
applications/simulations and measurement
methodology
Dr. Frederica Darema Senior Science and
Technology Advisor Director, Next Generation
Software Program NSF
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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
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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 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

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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/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 Grids
  measurement systems
• GRID Computing, and Beyond!!!

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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
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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/multisca
  le 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, tumors
• Through NGS
• Microarray Experiment Management System
• Ramakirishnan (V.Tech) PSE and Recommender
  System
• Through BITS
• Algorithms for RT Recording and Modulation of
  Neural Spike Trains
• Miller (U. Montana)

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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

• 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

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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
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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

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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-di
  ffusion equations)
• Collaboration with Michael Knopp, MD

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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)

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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)
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

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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

10-6
m
10-9
10-3
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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
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
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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
  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

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Research and Technology Roadmap (emphasis on
multidisciplinary research)
Application Composition System


Distributed programming models
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Application performance Interfaces
.
.
i

Compilers optimizing mappings on complex
systems
n
t
D
Providing

Application RunTime System
E
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enhanced
g

Automatic selection of solution methods
.
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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
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Measurement and instrumentation
n
Y1
Y2
Y3
Y4 Y5
Exploratory
Development
Integration Demos
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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
http//www.cise.nsf.gov/dddas
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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