1. 1 Dr. Frederica Darema Senior Science and Technology Advisor Director, Next Generation Software Program NSF Dynamic Data Driven Application Systems (DDDAS) A new paradigm for applications/simulations and measurement methodology

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

3. 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 & 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. 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. 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, and Beyond!!!

6. 6 What is Grid Computing? coordinated problem solving on dynamic and heterogeneous resource assemblies IMAGING INSTRUMENTS COMPUTATIONAL RESOURCES LARGE-SCALE DATABASES DATA ACQUISITION,ANALYSIS ADVANCED VISUALIZATION Example: “Telescience Grid”, Courtesy of Ellisman & Berman /UCSD&NPACI

7. 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. 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. 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. 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. 12 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:

13. 13 Data Management and Manipulation Visualization Field Measurements Simulation Models Reservoir Monitoring Field Implementation Data Analysis Production Forecasting Well Management Reservoir Performance Data Collections from Simulations and Field Measurements Economic Modeling and Well Management Multiple Realizations

14. 14 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

15. 15 Dynamic Contrast Imaging DCE-MRI (Osteosarcoma)

16. 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. 17 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

18. 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. 19 Virtual Microscope

20. 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. 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 -3 10 -6 10 -9 m

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

23. 23 ASP Test Problem: Pipe

24. 24 Pipe Workflow Ts t /Ps t Surface Mesh t Fluid Mesh t T4 Solid Mesh t Model t T10 Solid Mesh t Disps t Initial Flaw Params Surface Mesher Generalized Mesher JMesh T4  T10 Fluid/ThermoMechanical Crack Insertion Client: Crack Initiation Fracture Mechanics Crack Extension Growth Params 1 Model t+1 MiniCAD Viz

25. 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. 26 i e n t g r a t i o n Research and Technology Roadmap (emphasis on multidisciplinary research) Y1 Y2 Y3 Y4 Y5 ExploratoryDevelopment 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 D E M O S......... } } } i E n t g r a t i o n

27. 27 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

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

29. 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. 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