on Dynamic Data Driven Application Systems5 Some Technology Challenges in Enabling DDDAS...

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Dr. Frederica DaremaSenior Science and Technology Advisor

NSF

ICCS’06 WORKSHOPon

Dynamic Data Driven Application Systems(DDDAS)

A new paradigm forapplications/simulations and measurements methodology

(www.cise.nsf.gov/dddas & www.dddas.org)

INTRODUCTION and AGENDA

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Measurements Experiments

Field-DataUser

Theory

(First P

rinciple

s) Simula

tions

(Math.

Model

ing

Pheno

meno

log

y) ExperimentMeasurements

Field-Data(on-line/archival)

User

Theory

(First P

rinciple

s) Simula

tions

(Math.

Model

ing

Pheno

meno

logy

Obser

vation

Model

ing

Desig

n)OLD

(serialized a

nd static)

NEW PARAD

IGM

(Dynamic Data

-Driven Simula

tion Systems)

Challenges:Application SimulationsDevelopmentAlgorithmsMeasurement Instruments InterfacesComputing Systems Support

Dyna

mic

Feed

back

& C

ontro

l

Loop

What is DDDAS (Symbiotic Measurement&Simulation Systems)

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Examples of Areas of Impact

• Hazard prevention, mitigation and response– Earthquakes, hurricanes, tornados, wild fires, floods,

landslides, tsunamis, terrorist attacks• Critical infrastructure systems

– Condition monitoring and prediction of future capability• Transportation of humans and goods

– Safe, speedy, and cost effective transportation networksand vehicles (air, ground, space, water)

• Energy and environment– Safe and efficient power grids, safe and efficient operation

of regional collections of buildings

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Examples of Areas of Impact

• Health– Reliable and cost effective health care systems with

improved outcomes• Enterprise-wide decision making

– Coordination of dynamic distributed decisions for supplychains under uncertainty

• Next generation communication systems– Reliable wireless networks for homes and businesses

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Some Technology Challenges inEnabling 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

• Measurements– Multiple modalities, space/time distributed

• 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!!!

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What is Grid Computing?coordinated problem solving

on dynamic and heterogeneous resource assemblies

QuickTime™ and a decompressor

are needed to see this picture.



IMAGING INSTRUMENTS

COMPUTATIONALRESOURCES

LARGE-SCALE DATABASES

DATAACQUISITION ,ANALYSIS

ADVANCEDVISUALIZATION

Example: “Telescience Grid”, Courtesy of Ellisman & Berman /UCSD&NPACI

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DDDAS: Beyond Grid ComputingNew Capabilities in Applications and Measurements





COMPUTATIONALRESOURCES

DATAACQUISITION ,ANALYSIS

ADVANCEDVISUALIZATION

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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 (e.g.: parallel &grid computing,

numeric and non-numeric techniques: adaptive,asynchronous algorithms, dynamic meshing, dataassimilation, 3DVAR/4DVAR, chaotic Monte-Carlo)

• Ongoing DDDAS projects make advances in:– Applications, Algorithms, Instrumentation,

Systems Software

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DynamicallyLink

&Execute

The NGS Program developsTechnology for integrated feedback & controlRuntime Compiling System (RCS) and Dynamic Application Composition

ApplicationModel

Application Program

ApplicationIntermediate

Representation

CompilerFront-End

CompilerBack-End Performance

Measuremetns&

Models

DistributedProgramming

Model

ApplicationComponents

&Frameworks

Dynamic AnalysisSituation

LaunchApplication (s)

Distributed Platform

Adap

tabl

eco

mpu

ting

Syste

ms

Infra

struc

ture

Distributed Computing Resources

MPP NOW

SAR

tac-com

database

firecntl

firecntl

alg accelerator

database

SP

….

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Perform

ance

Enginee

ring

Dynam

ic

Compile

rs

&

Applica

tion

Compos

ition

Dynam

ic Data

-Drive

n

Applica

tion Sy

stems

--

Symbio

tic

Measu

rement

&Simu

lation

System

s

TowardsEnabling DDDAS

System

s Softw

are

MultidisciplinaryProgram

Applications

Modeling

& Measurements

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i

e

ntgratio

n

Research and Technology Roadmap (emphasis on multidisciplinary research)

Y1 Y2 Y3 Y4 Y5Exploratory Development

Integration & Demos

Application Composition System

Application RunTime System

Measurement System

Providing enhanced

capabilities for

Applications

DE

MO S

...

...

...

}

}

}

i

E

ntgratio

n

•Distributed programming models •Application performance interfaces•Dynamic compiler optimized mapping’•Debugging tools

•Application multi-resolution models•Automatic selection on solution methods•Modeling languages•Interfaces, data-representation & exchange

•Measurement instrumentation•Application interfaces•Measurement data formats/models

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DDDAS Funding OpportunitiesNSF/NGS, ITR, BITS Programs

-- Seeding DDDAS --• March 2000, 1st DDDAS Workshop• Starting in 2000 several NSF Programs have been used to seed

DDDAS: NGS, BITS, SES, ITR• ITR (FY00-FY04) Program was initiated to start new research, not

previously funded by existing NSF programs• ITR was used to spawn a significant number on ~DDDAS projects• majority of funds for DDDAS from ITR;

additional funding sources for other programs: NGS, SES, Sensors, BITS, etc

• About 40 projects• Project sizes: $400K-$12M, many $1-3M, one: $12M• ITR funds for DDDAS-related projects: ~$60M/cumulative• Funded projects are the success record for launching follow-on

DDDAS Program Solicitation in FY05• Workshops: NSF/2000; ICCS’03, ICCS’04; ICCS05

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Multi-Directorate/Multi-Agency DDDAS ProgramNSF, NIH, NOAA

with cooperation with the EU/IST & e-Sciences Programswww.cise.nsf.gov/dddas

NSF, NIH, NOAA Participants• National Science Foundation

Directorate for Computer and Information Science andEngineeringDirectorate for EngineeringDirectorate for Education and Human ResourcesDirectorate for Mathematical and Physical SciencesDirectorate for Social, Behavioral, and Economic SciencesOffice of International Science and EngineeringIndustrial Innovation Programs (SBIR/STTR)

• National Institutes of HealthNational Institute of General Medical SciencesNational Library of Medicine

• National Oceanic and Atmospheric AdministrationHigh Performance Computing and Communications Office

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International Collaborations

Cooperating International Programs• EU Information Society Technologies (IST) Programme

– EU e-Infrastructure– EU Grid Research

• RCUK: Research Councils UK– UK e-Science Program

In addition:The DDDAS Program Solicitation encourages internationalcollaborations in accordance with the portfolio of activitiesand scope of countries of the NSF Office ofInternational Science and Engineering

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Program Officials/ContactsNSF• Frederica Darema, Directorate for Computer & Information Science & Engineering• Mario Rotea, Directorate for Engineering• Marvin Goldberg, Directorate for Mathematical & Physical Sciences• Daniel H. Newlon, Directorate for Social, Behavioral & Economic Sciences• John C. Cherniavsky,, Directorate for Education & Human Resources• Juan E. Figueroa, SBIR/STTR Programs• Jeanne E. Hudson, Office of the Director, Office of International Science and Engineering,NIH• Charles Friedman, National Library of Medicine, Office of Extramural Programs• Peter Lyster, National Institute of General Medical SciencesNOAA• Robert Bohn, Project Manager, NOAAEU• Dr. Kyriakos Baxevanidis, EU e-Infrastructure Program• Dr. Max Lemke, Grid Research,under the EU Information Society Technologies (IST)

ProgrammeUK• Dr. James Flemming, e-Sciences RCUK Program

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Tally of FY05 DDDAS Program SolicitationAwarded Projects

• Projects solicited were required to have synergisticmultidisciplinary research in:– Applications, Math&Stat Algorithms, Systems Sw, and

Measurements• Categories of Projects sought&supported:

– “small”: spanning a primary and secondary area– “team”: span all four components above and have

substantial research in more than two components– “exploratory”: highly promising – high risk – high impact

–setting new directions in some area, exploratory work• 248 proposals received (June 13); total requested amount

$175M• 32 projects (48proposals) awarded; 15TMRP/13SMRP/4SEP;

total amount of funding $16M (US)

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DDDAS FY05 CompetitionProposals awarded (cont’d)

• Farhat - A Dynamic Data Driven System for Structural HealthMonitoring and Critical Event Prediction

• Ding - A Framework For the Dynamic Data-Driven Fault Diagnosis ofWind Turbine Systems

• Laidlaw - Interactive Data-driven Flow-simulation Parameter Refinementfor Understanding the Evolution of Bat Flight

• Rawlings - Measuring and Controlling Turbulence and ParticlePopulations

• Knight - SEP: Application of DDDAS to Assessment of Thermal SystemsUsing Combined Experiment and Simulation

• Barabasi- Integrated Wireless Phone Based Emergency ResponseSystem (WIPER)

• Fujimoto - Dynamic, Simulation-Based Management of SurfaceTransportation Systems

• Mahinthakumar - An adaptive cyberinfrastructure for threatmanagement in urban water distribution systems

+…

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• Ghattas - MIPS: A Real-Time Measurement-Inversion-Prediction-Steering Framework for Hazardous Events

• How - Coordinated Control of Multiple Mobile Observing Platforms forWeather Forecast Improvement

• Bernstein – Targeted Data Assimilation for Disturbance-DrivenSystems: Space weather Forecasting

• McLaughlin: - Data Assimilation by Field Alignment• Leiserson - Planet-in-a-Bottle: A Numerical Fluid-Laboratory• Chryssostomidis - Multiscale Data-Driven POD-Based Prediction of the

Ocean• Ntaimo - Dynamic Data Driven Integrated Simulation and Stochastic

Optimization for Wildland Fire Containment• Allen - DynaCode: A General DDDAS Framework with Coast and

Environment Modeling Applications• Douglas - Adaptive Data-Driven Sensor Configuration, Modeling, and

Deployment for Oil, Chemical, and Biological Contamination near CoastalFacilities

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• Clark - Dynamic Sensor Networks - Enabling the Measurement,Modeling, and Prediction of Biophysical Change in a Landscape

• Golubchik - A Generic Multi-scale Modeling Framework forReactive Observing Systems

• Williams - Real-Time Astronomy with a Rapid-Response TelescopeGrid

• Gilbert - Optimizing Signal and Image Processing in a Dynamic,Data-Driven Application System

• Liang - SEP: Intergrating Multipath Measurements with SiteSpecific RF Propagation Simulations

• Chen - SEP: Optimal interlaced distributed control anddistributed measurement with networked mobile actuators andsensors

+…

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DDDAS FY05 CompetitionProposals awarded

• Oden - Dynamic Data-Driven System for Laser Treatment of Cancer• Rabitz - Development of a closed-loop identification machine for

bionetworks (CLIMB) and its application to nucleotide metabolism• Fortes - Dynamic Data-Driven Brain-Machine Interfaces

• McCalley - Auto-Steered Information-Decision Processes for ElectricSystem Asset Management

• Downar - Autonomic Interconnected Systems: The National EnergyInfrastructure

• Sauer- Data-Driven Power System Operations

• Ball - Dynamic Real-Time Order Promising and Fulfillment for GlobalMake-to-Order Supply Chains

• Thiele – Robustness and Performance in Data-Driven RevenueManagement

• Son - Dynamically-Integrated Production Planning and OperationalControl for the Distributed Enterprise

Prior to FY05 DDDAS Program Solicitation:DDDAS-related Projects

Funded/Seededthrough NGS, BITS, SES, Sensors, ITR

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“~DDDAS” proposals awardedin FY00 ITR Competition

• Pingali, Adaptive Software for Field-Driven Simulations

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• Biegler – Real-Time Optimization for Data Assimilation and Controlof Large Scale Dynamic Simulations

• Car – Novel Scalable Simulation Techniques for Chemistry, MaterialsScience and Biology

• Knight – Data Driven design Optimization in Engineering UsingConcurrent Integrated Experiment and Simulation

• Lonsdale – The Low Frequency Array (LOFAR) – A Digital RadioTelescope

• McLaughlin – An Ensemble Approach for Data Assimilation in theEarth Sciences

• Patrikalakis – Poseidon – Rapid Real-Time Interdisciplinary OceanForecasting: Adaptive Sampling and Adaptive Modeling in aDistributed Environment

• Pierrehumbert- Flexible Environments for Grand-Challenge ClimateSimulation

• Wheeler- Data Intense Challenge: The Instrumented Oil Field ofthe Future

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• Carmichael – Development of a general Computational Framework forthe Optimal Integration of Atmospheric Chemical Transport Modelsand Measurements Using Adjoints

• Douglas-Ewing-Johnson – Predictive Contaminant Tracking UsingDynamic Data Driven Application Simulation (DDDAS) Techniques

• Evans – A Framework for Environment-Aware Massively DistributedComputing

• 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 BiologicalSystems

• Oden – Computational Infrastructure for Reliable ComputerSimulations

• Trafalis – A Real Time Mining of Integrated Weather Data

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• Baden – Asynchronous Execution for Scalable Simulation in CellPhysiology

• Chaturvedi– Synthetic Environment for Continuous Experimentation(Crisis Management Applications)

• Droegemeier-Linked Environments for Atmospheric Discovery(LEADS)

• Kumar – Data Mining and Exploration Middleware for Grid andDistributed Computing

• Machiraju – A Framework for Discovery, Exploration and Analysis ofEvolutionary Data (DEAS)

• Mandel – DDDAS: Data Dynamic Simulation for DisasterManagement (Fire Propagation)

• Metaxas- Stochastic Multicue Tracking of Objects with ManyDegrees of Freedom

• Sameh – Building Structural Integrity• {Sensors Program: Seltzer – Hourglass: An Infrastructure for

Sensor Networks}

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• Brogan – Simulation Transformation for Dynamic, Data-DrivenApplication Systems (DDDAS)

• Baldridge – A Novel Grid Architecture Integrating Real-TimeData and Intervention During Image Guided Therapy

• Floudas-In Silico De Novo Protein Design: A Dynamically DataDriven, (DDDAS), Computational and Experimental Framework

• Grimshaw: Dependable Grids• Laidlaw: Computational simulation, modeling, and visualization

for understanding unsteady bioflows• Metaxas – DDDAS - Advances in recognition and

interpretation of human motion: An Integrated Approach toASL Recognition

• Wheeler: Data Driven Simulation of the Subsurface:Optimization and Uncertainty Estimation

ICCS’06 DDDAS WorkshopAgenda

8-Sessions

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W17a: Dynamic Data Driven Application Systems; Session IMon, May 29; 10:20 - 12:00 ; Room: A/Mdjski

• Introduction – Frederica Darema• Towards Dynamic Data-Driven Management of the Ruby Gulch

Waste RepositoryManish Parashar, Vincent Matossian, Hector Klie, Sunil G. Thomas,Mary F. Wheeler, Tahsin Kurc, Joel Saltz, and Roelof Versteg

• Dynamic Contaminant Identification in WaterCraig C. Douglas, J. Clay Harris, Mohamed Iskandarani, Chris R.Johnson, Robert Lodder, Steve Parker, Martin J. Cole, RichardEwing, Yalchin Efendiev, Raytcho Lazarov, and Guan Qin:

• An Adaptive Cyberinfrastructure for Threat Management in UrbanWater Distribution SystemsKumar Mahinthakumar, Gregor von Laszewski, Ranji Ranjithan,Downey Brill, Jim Uber, Ken Harrison, Sarat Sreepathi, and EmilyZechman

• Model-Driven Dynamic Control of Embedded Wireless SensorNetworksPaul G. Flikkema, Pankaj K. Agarwal, James S. Clark, Carla Ellis,Alan Gelfand, Kamesh Munagala, and Jun Yang

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W17b: Dynamic Data Driven Application Systems; Session IIMon, May 29; 13:20 - 15:00; Room: A/Mdjski

• WIPER: The Integrated Wireless Phone Based Emergency ResponseSystemGreg R. Madey, Gabor Szabo, and Albert-László Barabási

• Dynamic Data Driven Application Simulation of SurfaceTransportation SystemsRichard M. Fujimoto, Randall Guensler, Michael Hunter, Hoe KyoungKim, Jaesup Lee, John Leonard II, Mahesh Palekar, KarstenSchwan, Balasubramanian Seshasayee

• DDDAS Approach to Fire and Agent Evacuation Modeling: CaseStudy of Rhode Island Nightclub FireAlok Chaturvedi, Angela Mellema, Sergei Filatyev, Jay Gore

• Auto- Steered Information-Decision Processes for Electric SystemAsset ManagementJames D McCalley, Vasant G Honavar, Sarah M Ryan, William QMeeker, Ronald A Roberts, Daji Qiao, Yuan Li

• Data-Driven Power System OperationsE. H. Abed, N. S. Nmachchivaya, T. J. Overbye, M. A. Pai, P. W.Sauer and A. Sussman

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W17c: Dynamic Data Driven Application Systems; Session IIIMon, May 29; 16:20 - 18:00 ; Room: Room: A/Mdjski

• Towards a Dynamic Data Driven System for Structural and MaterialHealth MonitoringC. Farhat, J. G. Michopoulos, F.K. Chang, L.J. Guibas, and A.J.Lew

• The Omni Macroprogramming Environment for Sensor NetworksAsad Awan, Ahmed Sameh, and Ananth Grama

• Evaluation of Fluid-Thermal Systems by Dynamic Data DrivenApplication SystemsD. Knight, T. Rossman and Y. Jaluria

• Inversion of Airborne Contaminants in a Regional ModelVolkan Akcelik, George Biros, Andrei Draganescu, Omar Ghattas,Judith Hill, and Bart van BloemenWaanders

• Localized Ensemble Kalman Data Assimilation for AtmosphericChemical Transport ModelsEmil M. Constantinescu, Adrian Sandu, Gregory R. Carmichael,Tianfeng Chai, John H. Seinfeld, and Dacian D¢aescu

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W17d: Dynamic Data Driven Application Systems; Session IVTue, May 24; 10:20 - 12:00; Room: A/Mdjski

• Data Assimilation Using the Global Ionosphere-Thermosphere ModelS. Kim, J. Chandrasekar, A. Ridley, D. S. Bernstein

• Amplitude-Position formulation of Data AssimilationSai Ravela

• Detection of Tornados Using an Incremental Revised Support VectorMachine with FiltersHyung-Jin Son and Theodore B. Trafalis

• A Generic Multi-scale Modeling Framework for Reactive ObservingSystems: an OverviewLeana Golubchik, David Caron, Abhimanyu Das, Amit Dhariwal,Ramesh Govindan, David Kempe, Carl Oberg, Abhishek Sharma, BethStauer, Gaurav Sukhatme, Bin Zhang

• Demonstrating the Validity of a Wildfire DDDASCraig C. Douglas, Jonathan D. Beezley, Janice Coen, Deng Li, WeiLi, Alan K. Mandel, Jan Mandel , Guan Qin, and Anthony Vodacek

W17g: Dynamic Data Driven System Applications Systems; Session VMon May 29; 18:00-18:40 Room:

Panel-1

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W17e: Dynamic Data Driven Application Systems; Session VITue, May 30; 13:20-15:00; Room: MSC W301

• Development of a Computational Paradigm for Laser Treatment ofCancerJ. T. Oden, K. R. Diller, C. Bajaj, J. C. Browne, J. Hazle2, I.Babu¡ska, J. Bass, L. Demkowicz, Y. Feng, D. Fuentes, S.Prudhomme, N. Rylander,R. J. Sta_ord, and Y. Zhang

• Blood Flow At Arterial Branches: Complexities To Resolve For TheAngioplasty Suite

P. D. Richardson, I.V. Pivkin, G.E. Karniadakis, D.H. Laidlaw• A New Architecture for Deriving Dynamic Brain-Machine Interfaces

José Fortes, Renato Figueiredo, Linda Hermer-Vazquez, JoséPríncipe and Justin C. Sanchez

• Dynamically Adaptive Tracking of Gestures and Facial ExpressionsD. Metaxas, G. Tsechpenakis, Z. Li, Y. Huang, A. Kanaujia

• Intelligent Management of Data Driven Simulations to Support Modelbuilding in the Social SciencesCatriola Kennedy and Georgios Theodoropoulos

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W17f: Dynamic Data Driven Application Systems; Session VIITue, May 30; 16:20 – 18:00; Room: MSC W301

• Capturing Scientists’ Insight for DDDASPaul Reynolds, David Brogan, Joseph Carnahan, Yannick Loiti`ere,and Michael Spiegel

• An MDA-based Modeling and Design of Service-OrientedArchitectureAdel Torkaman Rahmani, Vahid Rafe, Saeed Sedighian and AminAbbaspour

• Advanced Data Driven Visualisation for Geo-spatial DataAnthony Jones and Dan Cornford

• Design and Analysis of Test Signals for System Identification• Liu Bo, Zhao Jun, and Qian Jixin• The Research on the Method of Process-based Knowledge catalog &

Storage and its Application in Steel Product R&D• XiaoDong Gao and Zhiping Fan

Session VIII: 18:00-18:40Panel-2

•

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W16c: Dynamic Data Driven Application Systems IIITue, May 24; 3:40 PM - 5:40 PM; Room: MSC W301






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W16c: Dynamic Data Driven Application Systems Session 5Tue, May 24; 3:40 PM - 5:40 PM; Room: MSC W301

• Data Assimilation Using the Global Ionosphere-Thermosphere ModelS. Kim, J. Chandrasekar, A. Ridley, D. S. Bernstein

• Amplitude-Position formulation of Data AssimilationSai Ravela

• Detection of Tornados Using an Incremental Revised Support VectorMachine with FiltersHyung-Jin Son and Theodore B. Trafalis

• A Generic Multi-scale Modeling Framework for Reactive ObservingSystems: an OverviewLeana Golubchik, David Caron, Abhimanyu Das, Amit Dhariwal,Ramesh Govindan, David Kempe, Carl Oberg, Abhishek Sharma, BethStauer, Gaurav Sukhatme, Bin Zhang

• Demonstrating the Validity of a Wildfire DDDASCraig C. Douglas, Jonathan D. Beezley, Janice Coen, Deng Li, WeiLi, Alan K. Mandel, Jan Mandel , Guan Qin, and Anthony Vodacek

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W16c: Dynamic Data Driven Application Systems IIITue, May 24; 3:40 PM - 5:40 PM; Room: MSC W301






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