Presentation on theme: "1 Dr. Frederica Darema Senior Science and Technology Advisor Director, Next Generation Software Program NSF Dynamic Data Driven Application Systems (DDDAS)"— Presentation transcript:
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 … and how it would impact CyberInfrastructure!
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 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
4 NSF March 2000 Workshop on DDDAS (Co-Chairs: Craig Douglas, UKy; Abhi Desmukh, UMass) Invited Presentations 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 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
PETROLEUM APPLICATIONS G AS O IL W ATER F AULT S ALT D OME
6 Surface hydrophone array
7 Fire Model Sensible and latent heat fluxes from ground and canopy fire -> heat fluxes in the atmospheric model. Fires heat fluxes are absorbed by air over a specified extinction depth. 56% fuel mass -> H 2 0 vapor 3% of sensible heat used to dry ground fuel. Ground heat flux used to dry and ignite the canopy. Kirk Complex Fire. U.S.F.S. photo Slide Courtesy of Coen/NCAR
8 Coupled atmospheric and wildfire models Slide Courtesy of Coen/NCAR
9 AMAT Centura Chemical Vapor Deposition Reactor Operating Conditions Reactor Pressure1 atm Inlet Gas Temperature698 K Surface Temperature1173 K Inlet Gas-Phase Velocity46.6 cm/sec SiCl 3 H HCl + SiCl 2 SiCl 2 H 2 SiCl 2 + H 2 SiCl 2 H 2 HSiCl + HCl H 2 ClSiSiCl 3 SiCl 4 + SiH 2 H 2 ClSiSiCl 3 SiCl 3 H + HSiCl H 2 ClSiSiCl 3 SiCl 2 H 2 + SiCl 2 Si 2 Cl 5 H SiCl 4 + HSiCl Si 2 Cl 5 H SiCl 3 H + SiCl 2 Si 2 Cl 6 SiCl 4 + SiCl 2 Gas Phase Reactions SiCl 3 H + 4s Si(B) + sH + 3sCl SiCl 2 H 2 + 4s Si(B) + 2sH + 2sCl SiCl 4 + 4s Si(B) + 4sCl HSiCl + 2s Si(B) + sH + sCl SiCl 2 + 2s Si(B) + 2sCl 2sCl + Si(B) SiCl 2 + 2s H 2 + 2s 2sH 2sH 2s + H 2 HCl + 2s sH + sCl sH + sCl 2s + HCl Surface Reactions Slide Courtesy of McRae/MIT
10 MSTAR (DARPA) (Moving and Stationary Target Acquisition and Recognition) Predict Extract Focus of Attention SAR Image & Collateral Data - DTED, DFAD - Site Models - EOSAT imagery ROAD TREES GRASS H2OH2O Regions of Interest (ROI) Segmented Terrain Map Indexing... Search Tree... Index Database (created off-line) Search Target & Scene Model Database (created off line) Match Local Scene Map GRASS TREES ROI Hypothesis x y BMP-2 Shadow (?) Match Results Score = 0.75 Form Associations Refine Pose & Score x1,y1, x2,y2, Analyze Mismatch Tree Clutter Ground Clutter Shadow Obscuration ? Feature-to-Model Traceback Task PredictTask Extract Local Scene Map GRASS TREES ROI Hypothesis x y BMP- 2 Target & Clutter Database Statistical Model CAD Semantic Tree Clutter Database
11 Application Integration Interoperability The e-Business / (CIM, CIE) Enterprise Messaging Manufacturing Product DBs Inventory Shipping Order Processing Customer Service Sales Management Process Coordination Management & Monitoring Data Integration Interoperability Distributor Channel Business to Business Web e-commerce Business to Customer Mobile Workers Knowledge Workers Business Communications
12 Compare with Classical (Old) Supply Chain Parts Supplier ManufacturingDistributionRetail Customer ManufacturingDistributionRetail Customer ManufacturingDistributionRetail Customer Parts Supplier Transportation Supplier
13 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 –GRID Computing, and Beyond!!!
14 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
15 Dynamically Link & Execute The NGS Program developsTechnology for integrated feedback & control Runtime Compiling System (RCS) and Dynamic Application Composition Application Model Application Program Application Intermediate Representation Compiler Front-End Compiler Back-End Performance Measuremetns & Models Distributed Programming Model Application Components & Frameworks Dynamic Analysis Situation Launch Application (s) Distributed Platform Adaptable computing Systems Infrastructure Distributed Computing Resources MPPNOW SAR tac-com data base fire cntl fire cntl alg accelerator data base SP ….
16 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)
17 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
18 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)
19 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
20 Performance Engineering Dynamic Compilers & Application Composition Dynamic Data-Driven Application Systems -- Symbiotic Measurement&Simulation Systems Towards Enabling DDDAS NGS Program Todays Grid Environments: Users shouldnt Have to be Heroes to Achieve Grid Program Performance and... because heroism is not enough
21 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
22 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 –Algorithms
23 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!
24 ~DDDAS proposals awarded in FY00 ITR Competition Pingali, Adaptive Software for Field-Driven Simulations
25 ~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
26 ~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
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 Partners Institute for Defense Analyses Office of VP IT, Purdue University Research and Academic Computing, Indiana University Simulex, Inc
28 Parallel Worlds Real World Environment Explore, Experiment, Learn, Analyze, Test, & Anticipate Implement, Assess Behavior modeling, demographics, and calibration Data collection, association, trends, and parameter estimation Time Compression Near exact replica of the real world SEAS architecture Supports millions of Artificial agents Decision Support Loop Synthetic Environment The user(s) can seamlessly switch between real and virtual worlds through an intuitive user interface. SCM ERP CRM Data Warehouse Simulation Loop
29 Reproduction Model Susceptible Mortality Exposed Infected w/o Symptoms Infected w/ Symptoms Immune recovered Succumb to the disease mortality not due to infection entering incubation period end of incubation period Interventions: Screen, Isolate (camp or shelter), Treat, Vaccinate Get in contact with infected Uninfected Immunized
30 Mobility Models Regular Movement Event Traffic Morning and Evening Rush Evacuation Panic Fleeing
31 New Infections No Intervention T4 Intervention T2 Intervention T6 Intervention
32 Towards a National Grid for HLS Real World The virtual world Data Fusion Bio sensor Nano Sensor MEMS electronic human
33 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
34 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:
35 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
36 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
38 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
39 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
40 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
41 1370 1421 1438 prior to therapy after 2 cycles after 4 cycles 1370 1421 1438 Knopp M, OSU Radiology / dkfz
42 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
43 Virtual Microscope
44 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
45 SCOPE of ASP 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: Theyre Everywhere!
46 ASP Test Problem
47 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: –simulate flows –determine crack growth –couple the multi-physics models –When successful add the ability to inject monitoring measurements
48 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
49 Chemically-reacting flows MSU/UAB expertise in chemically-reacting flows LOCI: system for automatic synthesis of multi-disciplinary simulations
50 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
51 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
52 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
53 Interdisciplinary Ocean Science
54 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.)
55 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%).
56 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.
57 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.
58 Data Assimilation for Chemical Models Solid lines represent current capabilities Dotted lines represent new analysis capabilities Future: enable DDDAS capabilities
59 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.
60 Modeling 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 Irreducible versus epistemic uncertainty …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
61 Partially Correlated non-Uniform Random Inflow Vorticity: Regions of Uncertainty Pressure Deterministic Stochastic
62 Non-uniform Gaussian Random BC U mean along centerlineV mean along centerline Exponential correlation Stochastic input: 2D K-L expansion 4 th -order Hermite-Chaos expansion 15-term expansion
63 Non-uniform Exponential Random BC U mean along centerlineV mean along centerline Exponential correlation Stochastic input: 2D K-L expansion 4 th -order Laguerre-Chaos expansion 15-term expansion
64 Research Opportunities in Uncertainty lUUncertainty analysis is a fertile and much needed area for inter-disciplinary research lEEstimates of uncertainties in model inputs are desperately needed Uncertainty Ignorance
65 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
66 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
67 DDDAS: http://www.cise.nsf.gov/dddas http://www.dddas.org NGS: http://www.cise.nsf.gov/div/acir 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
68 Following is a List of Presentations of DDDAS projects at the International Conference on Computational Sciences June 2-6, 2003, Melbourne Australia
69 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
70 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
71 Dynamic Data Driven Application Systems WORKSHOP Tues June 3 (contd) 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 Sellers Behavior in a Reverse Auction B2B Exchange Alok Chaturvedi, Purdue U.