Presentation is loading. Please wait.

Presentation is loading. Please wait.

Dynamic Data Driven Application Systems (DDDAS) A new paradigm for

Similar presentations

Presentation on theme: "Dynamic Data Driven Application Systems (DDDAS) A new paradigm for"— Presentation transcript:

1 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 NSF

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

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 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:


6 Surface hydrophone array
Source: Schlumberger online oilfield glossary at

7 Fire Model Sensible and latent heat fluxes from ground and canopy fire -> heat fluxes in the atmospheric model. 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. 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 Pressure 1 atm Inlet Gas Temperature 698 K Surface Temperature 1173 K Inlet Gas-Phase Velocity 46.6 cm/sec 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 Gas Phase Reactions 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 Surface Reactions Slide Courtesy of McRae/MIT

10 MSTAR (DARPA) (Moving and Stationary Target Acquisition and Recognition)
ROAD TREES GRASS H2O Focus of Attention Index Database (created off-line) Search Tree ... Target & Clutter Database Regions of Interest (ROI) Segmented Terrain Map SAR Image & Collateral Data - DTED, DFAD - Site Models - EOSAT imagery ... Local Scene Map GRASS TREES ROI Hypothesis x y BMP-2 Indexing Target & Scene Model Database (created off line) Task Predict Task Extract Local Scene Map GRASS TREES ROI Hypothesis x y BMP-2 Shadow (?) Statistical Model Predict Search Extract Clutter Database CAD Match Results Tree Clutter Semantic Tree Form Associations Refine Pose & Score Analyze Mismatch Shadow Obscuration ? x1,y1,  x2,y2,  Score = 0.75 Ground Clutter Feature-to-Model Traceback Match

11 The e-Business / (CIM, CIE)
Distributor Channel Order Processing Customer Service Sales Management Manufacturing Product DBs Inventory Shipping Application Integration Interoperability Process Coordination Management & Monitoring Business to Enterprise Messaging Data Integration Interoperability Mobile Workers Knowledge Workers Business Communications Business to Customer Web e-commerce

12 Compare with Classical (Old) Supply Chain
Manufacturing Distribution Retail Customer Parts Supplier Manufacturing Distribution Retail Customer Parts Supplier Manufacturing Distribution Retail Customer 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

15 The NGS Program developsTechnology for integrated feedback & control Runtime Compiling System (RCS) and Dynamic Application Composition Dynamic Analysis Situation Application Model Distributed Programming Model Application Program Compiler Front-End Application Intermediate Representation Compiler Back-End Launch Application (s) Performance Measuremetns & Models Dynamically Link & Execute Application Components & Frameworks Distributed Computing Resources Distributed Platform computing Systems Adaptable Infrastructure MPP NOW SAR tac-com base data cntl fire alg accelerator 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 Towards Enabling DDDAS
“Users shouldn’t Have to be Heroes Today’s Grid Environments: to Achieve Grid Program Performance” and... because heroism is not enough Dynamic Data-Driven Application Systems Measurement&Simulation Symbiotic -- Systems Dynamic Compilers Application & Composition NGS Program Performance Engineering

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: 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, ) 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; 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

27 Measured Response Partners 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

28 Explore, Experiment, Learn, Analyze, Test, & Anticipate
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 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 Interventions:
Get in contact with infected Infected w/o Symptoms Susceptible Exposed entering incubation period Uninfected Immunized end of incubation period mortality not due to infection Immune recovered Infected w/ Symptoms Mortality Succumb to the disease Interventions: Screen, Isolate (camp or shelter), Treat, Vaccinate

30 Mobility Models Regular Movement Event Traffic
Morning and Evening Rush Evacuation Panic Fleeing

31 New Infections T6 Intervention No Intervention T2 Intervention

32 Towards a National Grid for HLS
Data Fusion Bio sensor human MEMS The virtual world Nano Sensor electronic Real World

33 A Data Intense Challenge: The Instrumented Oilfield of the Future
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
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

35 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

36 A Data Intense Challenge: The Instrumented Oilfield of the Future
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

37 Dynamic Contrast Imaging DCE-MRI (Osteosarcoma)

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 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)

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 Knopp M, OSU Radiology / dkfz
prior to therapy 1370 1370 after 2 cycles 1421 1421 1421 Diese Datei und alle darin enthaltenen Bilder sind Eigentum von Michael V. Knopp. Jede Benutzung, Veröffentlichung oder Weitergabe gleich welcher Art ist nicht statthaft, wenn dies nicht ausdrücklich und schriftlich genehmigt wurde. Fragen und Mitteilungen an This is protected information. Any copies or distribution without the express written approval of Dr. M. Knopp is prohibited after 4 cycles 1438 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 Cracks: They’re Everywhere!
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

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 ( microns) poly-crystals Micro-scale ( 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 MiniCAD Surface Mesher Surface Mesht Generalized Mesher
Modelt JMesh T4 Solid Mesht Fluid Mesht Mechanical Tst/Pst Fluid/Thermo T4T10 Client: Crack Initiation T10 Solid Mesht Initial Flaw Params Crack Insertion Dispst Modelt+1 Fracture Mechanics Growth Params1 Crack Extension Viz

51 Adaptive Sampling and Adaptive Modeling
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

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
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

61 Partially Correlated non-Uniform Random Inflow
Deterministic Stochastic Pressure Vorticity: Regions of Uncertainty

62 Non-uniform Gaussian Random BC
Exponential correlation Stochastic input: 2D K-L expansion 4th-order Hermite-Chaos expansion 15-term expansion Umean along centerline Vmean along centerline

63 Non-uniform Exponential Random BC
Exponential correlation Stochastic input: 2D K-L expansion 4th-order Laguerre-Chaos expansion 15-term expansion Umean along centerline Vmean along centerline

64 Uncertainty  Ignorance
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

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 (emphasis on multidisciplinary research)
Research and Technology Roadmap (emphasis on multidisciplinary research) i e n t g r a o Application Composition System } Distributed programming models . Application performance Interfaces . Compilers optimizing mappings on complex . i systems n t D Application RunTime System } E Providing E enhanced Automatic selection of solution methods . g Interfaces, data representation & exchange . M . capabilities Debugging tools r O for S a Applications t Measurement System } i Application/system multi-resolution models . o Modeling languages . . Measurement and instrumentation n Y1 Y2 Y3 Y Y5 Exploratory Development Integration & Demos

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: NGS:

68 backup slides 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 (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.

Download ppt "Dynamic Data Driven Application Systems (DDDAS) A new paradigm for"

Similar presentations

Ads by Google