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Dr. Frederica Darema Senior Science and Technology Advisor

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. What is DDDAS. OLD (serialized and static ).

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Dr. Frederica Darema Senior Science and Technology Advisor

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  1. DynamicData 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

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

  3. Examplesof 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

  4. NSF Workshop on DDDAS • 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: www.cise.nsf.gov/dddas

  5. 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, andBeyond!!!

  6. 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 /UCSD&NPACI

  7. 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

  8. 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!

  9. “~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)

  10. Examples of DDDAS efforts

  11. 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

  12. 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 simulationand 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

  13. 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

  14. 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

  15. Dynamic Contrast ImagingDCE-MRI (Osteosarcoma)

  16. 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

  17. 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)

  18. 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

  19. Virtual Microscope

  20. 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!

  21. 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

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

  23. ASP Test Problem: Pipe

  24. Pipe Workflow MiniCAD SurfaceMesher SurfaceMesht GeneralizedMesher JMesh Modelt T4 SolidMesht FluidMesht Mechanical Tst/Pst Fluid/Thermo T4T10 Client: CrackInitiation T10 SolidMesht Initial FlawParams CrackInsertion Dispst Modelt+1 FractureMechanics GrowthParams1 CrackExtension Viz

  25. 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

  26. Research and Technology Roadmap (emphasis on multidisciplinary research) i n t e g r a t i o n 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 . M • Interfaces, data representation & exchange 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

  27. 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

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

  29. “~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

  30. “~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

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