Dynamic Data Driven Application Systems (DDDAS) A new paradigm for applications/simulations
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Dynamic Data Driven Application Systems (DDDAS) A new paradigm for applications/simulations and measurement methodology … and how it would impact CyberInfrastructure! Dr. Frederica Darema Senior Science and Technology Advisor Director, Next Generation Software Program 1 NSF What is DDDAS Measurements Experiment Field-Data User 2 (Symbiotic Measurement&Simulation Systems) Experiment Measurements Field-Data (on-line/archival) User Challenges: Application Simulations Development Algorithms Computing Systems Support 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 3 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 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 4 PETROLEUM APPLICATIONS SALT DOME FAULT GAS OIL WATER Surface hydrophone array 6 Fire Model • Sensible and latent heat fluxes from ground and canopy fire -> heat fluxes in the atmospheric model. • Ground heat flux used to dry and ignite the canopy. • 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. Kirk Complex Fire. U.S.F.S. photo 7 Slide Courtesy of Coen/NCAR Coupled atmospheric and wildfire models 8 Slide Courtesy of Coen/NCAR AMAT Centura Chemical Vapor Deposition Reactor Operating Conditions Reactor Pressure Inlet Gas Temperature Surface Temperature Inlet Gas-Phase Velocity 1 atm 698 K 1173 K 46.6 cm/sec Surface Reactions Gas Phase Reactions SiCl3H HCl + SiCl2 SiCl2H2 SiCl2 + H2 SiCl2H2 HSiCl + HCl H2ClSiSiCl3 SiCl4 + SiH2 H2ClSiSiCl3 SiCl3H + HSiCl H2ClSiSiCl3 SiCl2H2 + SiCl2 Si2Cl5H SiCl4 + HSiCl Si2Cl5H SiCl3H + SiCl2 Si2Cl6 SiCl4 + SiCl2 SiCl3H + 4s Si(B) + sH + 3sCl SiCl2H2 + 4s Si(B) + 2sH + 2sCl SiCl4 + 4s Si(B) + 4sCl HSiCl + 2s Si(B) + sH + sCl SiCl2 + 2s Si(B) + 2sCl 2sCl + Si(B) SiCl2 + 2s H2 + 2s 2sH 2sH 2s + H2 HCl + 2s sH + sCl sH + sCl 2s + HCl Slide Courtesy of McRae/MIT 9 MSTAR (DARPA) (Moving and Stationary Target Acquisition and Recognition) TREES Focus of Attention H2O Index Database (created off-line) GRASS ROAD ... Segmented Terrain Map .. . Regions of Interest (ROI) Target & Clutter Database SAR Image & Collateral Data - DTED, DFAD - Site Models - EOSAT imagery Search Tree ROI Hypothesis y GRASS BMP2 Indexing Local Scene Map x Target & Scene Model Database (created off line) TREES TREES Task Predict Task Extract ROI Hypothesis Predict Shadow (?) y BMP-2 x CAD TREES GRASS TREES Statistical Model Search Extract Local Scene Map Match Results Semantic Tree Clutter Database Form Associations Refine Pose & Score Analyze Mismatch Tree Clutter Shadow Obscuration ? x1,y1, x2,y2, Score = 0.75 Feature-to-Model Traceback 10 Match Ground Clutter The e-Business / (CIM, CIE) Order Processing Customer Service Sales Management Process Coordination Management & Monitoring Manufacturing Product DBs Inventory Shipping Enterprise Messaging Business to Customer Web e-commerce 11 Business to Business Mobile Workers Knowledge Workers Business Communications Distributor Channel Compare with Classical (Old) Supply Chain Parts Supplier Parts Supplier Manufacturing Distribution Retail Customer Customer Manufacturing Distribution Retail Customer Customer Manufacturing Distribution Retail Customer Customer Transportation Supplier 12 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 – 13GRID Computing, and Beyond!!! What is Grid Computing? coordinated problem solving on dynamic and heterogeneous resource assemblies DATA ACQUISITION ADVANCED VISUALIZATION ,ANALYSIS QuickTime™ and a decompressor are needed to see this picture. COMPUTATIONAL RESOURCES IMAGING INSTRUMENTS LARGE-SCALE DATABASES Example: “Telescience Grid”, Courtesy of Ellisman & Berman /UCSD&NPACI 14 The NGS Program developsTechnology for integrated feedback & control Runtime Compiling System (RCS) and Dynamic Application Composition Application Model Dynamic Analysis Situation Distributed Programming Model Application Program Compiler Front-End Application Intermediate Representation Compiler Back-End Launch Application (s) Dynamically Link & Execute Performance Measuremetns & Models Application Components & Frameworks Distributed Computing Resources Distributed Platform tac-com alg accelerator …. 15 MPP NOW fire cntl data base data base fire cntl SAR SP 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) 16 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 17 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) 18 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 19 20 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 21 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 – 22Algorithms 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! 23 “~DDDAS” proposals awarded in FY00 ITR Competition • Pingali, Adaptive Software for Field-Driven Simulations 24 “~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 25 “~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 26 Measured Response A Homeland Security Simulation (Briefed WH 5/14/02) Alok Chaturvedi, Director Shailendra 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 Parallel Worlds Time Compression Simulation Loop Decision Support Loop Near exact replica of the “real” world Synthetic Environment Behavior modeling, demographics, and calibration SEAS architecture Supports millions of Artificial agents Explore, Experiment, Learn, Analyze, Test, & Anticipate Real World Environment SCM ERP CRM Data Warehouse Data collection, association, trends, and parameter estimation Implement, Assess The user(s) can seamlessly switch between real and virtual worlds through an intuitive user interface. 28 Reproduction Model Get in contact with infected Susceptible Exposed Infected w/o Symptoms Immunized Uninfected entering incubation period end of incubation period Immune Infected w/ Symptoms Mortality Succumb to the disease Interventions: Screen, Isolate (camp or shelter), Treat, Vaccinate 29 Mobility Models • • • • • 30 Regular Movement Event Traffic Morning and Evening Rush Evacuation Panic Fleeing New Infections 400 600 No Intervention 350 T6 Intervention 500 300 400 250 200 300 150 200 100 100 50 0 0 1 2 3 4 5 6 Local State 7 8 9 10 1 11 2 3 4 5 6 Local State Federal 250 7 8 9 10 11 Federal 120 T2 Intervention T4 Intervention 100 200 80 150 60 100 40 50 20 0 0 1 2 31 3 4 5 Time 4 Intervention Local 6 Time 4 Intervention State 7 8 Time 4 Intervention Federal 9 10 11 1 2 3 4 5 Series1 6 Series2 Series3 7 8 9 10 Towards a National Grid for HLS Data Fusion Bio sensor human MEMS electronic Nano Sensor Real World 32 The virtual world NSF ITR Project A Data Intense Challenge: The Instrumented Oilfield of the Future PI: Prof. Mary Wheeler, UT Austin Multi-Institutional/Multi-Researcher Collaboration 33 Slide Courtesy of Wheeler/UTAustin Highlights of Instrumented Oilfield Proposal III. IT Technologies: Data management, data visualization, parallel computing, and decision-making tools such as new wave propagation and multiphase, multicomponent flow and transport computational portals, reservoir production: THE INSTRUMENTED OILFIELD 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 34 Economic Modeling and Well Management Production Forecasting Well Management Reservoir Performance Data Analysis Simulation Models Multiple Realizations Data Management and Manipulation Data Collections from Simulations and Field Measurements 35 Visualization Field Measurements Reservoir Monitoring Field Implementation ITR Project A Data Intense Challenge: The Instrumented Oilfield of the Future II. 36 Industrial Support (Data): i. British Petroleum (BP) ii. Chevron iii. International Business Machines (IBM) iv. Landmark v. Shell vi. Schlumberger Dynamic Contrast Imaging DCE-MRI (Osteosarcoma) 37 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-convectiondiffusion equations) – Collaboration with Michael Knopp, MD 38 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) 39 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 40 prior to therapy 1370 1370 after 2 cycles 1421 1421 1438 1438 after 4 cycles Knopp 41 M, OSU Radiology / dkfz 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 42 Virtual Microscope 43 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 44 SCOPE of ASP Cracks: They’re 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 45 ASP Test Problem 46 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: – – – – 47 simulate flows determine crack growth couple the multi-physics models When successful add the ability to inject monitoring measurements 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 48 10-6 10-9 m Chemically-reacting flows • MSU/UAB expertise in chemically-reacting flows • LOCI: system for automatic synthesis of multi-disciplinary simulations 49 Pipe Workflow MiniCAD Modelt Mechanical Dispst Viz 50 Surface Mesher Tst/Pst Surface Mesht Fluid/Thermo Generalized Mesher Fluid Mesht Client: Crack Initiation Initial Flaw Params Crack Insertion Fracture Mechanics Growth Params1 Crack Extension JMesh T4 Solid Mesht T4T10 T10 Solid Mesht Modelt+1 Poseidon Rapid Real-Time Interdisciplinary Ocean Forecasting: Adaptive Sampling and Adaptive Modeling in a Distributed Environment Nicholas M. Patrikalakis, Henrik Schmidt, MIT Allan R. Robinson, James J. McCarthy, Harvard http://czms.mit.edu/poseidon 51 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 52 Interdisciplinary Ocean Science 53 Development of a 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.) 54 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 Experiment Will 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%). 55 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. 56 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. 57 Data Assimilation for Chemical Models Solid lines represent current capabilities Dotted lines represent new analysis capabilities Future: enable DDDAS capabilities 58 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. 59 Modeling Uncertainty Irreducible versus epistemic 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 “…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 60 Partially Correlated non-Uniform Random Inflow •Deterministic •Stochastic •Pressure Vorticity: Regions of Uncertainty 61 Non-uniform Gaussian Random BC • Exponential correlation C( x 1 , x 2 ) 2 e x x 1 • Stochastic input: 0.1 Umean along centerline 62 • 2D K-L expansion 2 /b • 4th-order Hermite-Chaos expansion • 15-term expansion Vmean along centerline Non-uniform Exponential Random BC • Exponential correlation C( x 1 , x 2 ) 2 e x x 1 • Stochastic input: 0.1 Umean along centerline 63 • 2D K-L expansion 2 /b • 4th-order Laguerre-Chaos expansion • 15-term expansion Vmean along centerline Research Opportunities in Uncertainty UUncertainty analysis is a fertile and much needed area for inter-disciplinary research EEstimates of uncertainties in model inputs are desperately needed Uncertainty Ignorance 64 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 65 Research and Technology Roadmap (emphasis on multidisciplinary research) 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 .. . Y1 i n t e g r a t i o n Y2 Exploratory i n t D E Eg r a t i o n Y3 Y4 M O Y5 Development Integration & Demos 66 S Providing enhanced capabilities for Applications 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 DDDAS: http://www.cise.nsf.gov/dddas http://www.dddas.org NGS: http://www.cise.nsf.gov/div/acir 67 Following is a List of Presentations of DDDAS projects at the International Conference on Computational Sciences June 2-6, 2003, Melbourne Australia 68 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 Uncertainties Greg 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 69 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 Heterogeneity James Nutaro – U of Arizona 70 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 XML Constantinos 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 Model John Miller, Montana State U. Simulating Seller’s Behavior in a Reverse Auction B2B Exchange Alok Chaturvedi, Purdue U. 71