Dynamic Data Driven Applications Systems (DDDAS) Program PI Meeting Date: Sept 30 – Oct 2, 2013 East/West Falls Church Room,VTech/BRICC 900 North Glebe Road, Arlington, VA 22203 Frederica Darema, Ph. D., IEEE Fellow AFOSR Air Force Research Laboratory Integrity Service Excellence 1 Distribution A: Approved for Public Release, Unlimited Distribution
DDDAS Program PI Meeting Overview • The Objectives of the Meeting: • overview of the status of the projects • highlight accomplishments • explore opportunities for cross-leverage of progress across projects • explore opportunities for cross-leverage of other US and international programmatic activities • discuss directions and opportunities for the future • Agenda: • 3-day meeting • individual project presentations with Q&A • plenary discussions on groups of projects 2 Distribution A: Approved for Public Release, Unlimited Distribution
Dynamic Data Driven Applications Systems (DDDAS) InfoSymbiotic Systems DDDAS: ability to dynamically incorporate additional data into an executing application, and in reverse, ability of an application to dynamically Measureme ment nts steer the measurement process Exper erime ment nts Field-Dat ata “revolutionary” concept enabling User design, build, manage, understand complex systems Dynamic Integration of Computation & Measurements/Data Unification of Computing Platforms & Sensors/Instruments (from the High-End to the Real-Time,to the PDA) DDDAS – architecting & adaptive mngmnt of sensor systems Challenges : Application Simulations Methods Experiment Algorithmic Stability Measurements Measurement/Instrumentation Methods Field-Data Computing Systems Software Support (on-line/archival) Dynamic User Feedback & Control Synergistic, Multidisciplinary Research Loop F. Darema 3 Distribution A: Approved for Public Release, Unlimited Distribution
Advances in Capabilities through DDDAS DDDAS - Clearly articulated concept/paradigm: • integration of application simulation/models with the application instrumentation components in a dynamic feed-back control loop speedup of the simulation, by replacing computation with data in specific parts of the phase-space of the application and/or augment model with actual data to improve accuracy of the model, improve analysis/prediction capabilities of application models enable ~decision-support capabilities w simulation-modeling accuracy dynamically manage/schedule/architect heterogeneous resources, such as: networks of heterogeneous sensors, or networks of heterogeneous controllers increased computation/communication capabilities; ubiquitous heterogeneous sensing • unification from the high-end to the real-time data acquisition and control DDDAS is more powerful and broader paradigm than Cyber-Physical Systems 4 Distribution A: Approved for Public Release, Unlimited Distribution
DDDAS/AFOSR BAA and Technology Horizons • Context of Key Strategic Approaches of the Program – Multidisciplinary Research – Focus of advancing capabilities along the Key Areas identified in the Technology Horizons, and the Energy Horizons and Global Horizons Reports DDDAS … key concept in many of the objectives set in Technology Horizons Top KTAs identified in the 2010 Technology Horizons Report • • • Autonomous systems • Spectral mutability Autonomous systems Spectral mutability • • • Autonomous reasoning and learning • Dynamic spectrum access Autonomous reasoning and learning Dynamic spectrum access • • • Resilient autonomy • Quantum key distribution Resilient autonomy Quantum key distribution • • • • Complex adaptive systems Multi-scale simulation technologies Complex adaptive systems Multi-scale simulation technologies • • • • V&V for complex adaptive systems Coupled multi-physics simulations V&V for complex adaptive systems Coupled multi-physics simulations • • • Collaborative/cooperative control • Embedded diagnostics Collaborative/cooperative control Embedded diagnostics • • • Autonomous mission planning • Decision support tools Autonomous mission planning Decision support tools • • • Cold-atom INS • Automated software generation Cold-atom INS Automated software generation • • • • Chip-scale atomic clocks Sensor-based processing Chip-scale atomic clocks Sensor-based processing • • • • Ad hoc networks Behavior prediction and anticipation Ad hoc networks Behavior prediction and anticipation • • • • Polymorphic networks Cognitive modeling Polymorphic networks Cognitive modeling • • • Agile networks • Cognitive performance augmentation Agile networks Cognitive performance augmentation • • • Laser communications • Human-machine interfaces Laser communications Human-machine interfaces 5 • Distribution A: Approved for Public Release, Unlimited Distribution • Frequency-agile RF systems Frequency-agile RF systems
AFOSR DDDAS Program DDDAS Program Research Components • Application Modeling/Simulation • Application Algorithms • Systems Software • Instrumentation methods • Program announced in AFSOR BAA-2011 (posted in Spring2011) • Projects awarded in FY11, FY12, FY13 • Additional proposals have been submitted and are reviewed for FY14 funding; • Additional WP and proposals are expected to by submitted inFY14 6 Distribution A: Approved for Public Release, Unlimited Distribution
PI Meeting Agenda Day 1 – September 30, 2013 Day 1 – Morning Session 8:00am-8:30am – Introduction to the Program - Overview of PI Meeting-- Darema 8:30am-10:30am Materials modeling • Development of a Stochastic Dynamic Data-Driven System for Prediction of Materials Damage – PI: Tinsley Oden (UT Austin), and Team • Developing Data-Driven Protocols to study Complex Systems: The case of Engineered Granular Crystals (EGC) – PI: Yannis Kevrekidis (Princeton Univ) , and Team • Dynamic Data-Driven Modeling of Uncertainties and 3D Effects of Porous Shape Memory Alloys – PI: Craig Douglas (U of Wyoming), and Team • Dynamic, Data-Driven Modeling of Nanoparticle Self Assembly Processes – Y. Ding (TAMU),and Team 10:30am-10:45am --Break 10:45am-12:15pm AirVehicle Structural HealthMonitoring – Environment Cognizant • Advanced Simulation, Optimization, and Health Monitoring of Large Scale Structural Systems – PI: Yuri Bazilevs (UCSD), and Team • Dynamic Data-Driven Methods for Self-Aware Aerospace Vehicles – PI: Karen Willcox (MIT), and Team • Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring – PI: Thomas Henderson (U. of Utah) 12:15pm-1:00pm --Lunch 7 Distribution A: Approved for Public Release, Unlimited Distribution
PI Meeting Agenda Day 1 – September 30, 2013 Day 1 – Afternoon Session 1:00pm -2:00pm Energy Efficiencies Energy-Aware Aerial Systems for Persistent Sampling and Surveillance – PI: Erik Frew (U of Colorado-Boulder), and Team • DDDAMS-based Real-time Assessment and Control of Electric-Microgrids – PI: Nurcin Celik (University of Miami) 2:00pm -3:00pm Spatial Situational Awareness (UAV Swarms + Ground Systems Coordination) • Application of DDDAS Principles to Command, Control and Mission Planning for UAV Swarms – PI: Greg Madey (U. Of Notre Dame), and Team • DDDAMS-based Urban Surveillance and Crowd Control via UAVs and UGVs – PI: Young-Jun Son ( University of Arizona),, and Team 3:00pm -3:15pm --Break 3:15pm -4:45pm (UAV Swarms + Ground Systems Coordination) – New Starts (15mins each) • Dynamic Systems for Individual Tracking via Heterogeneous Information Integration and Crowd Source Distributed Simulation – PI: Richard Fujimoto (Georgia Tech), and Team • An Integrated approach to the Space Situational Awareness Problem – PI: Suman Chakravorty (TAMU) , and Team • A Dynamic Data Driven Cognitive Control Architecture for Exploration – PI: Jose Principe (U. of Florida), and Team 4:45pm -5:30pm – Discussion of all projects discussed in Day 1 8 Distribution A: Approved for Public Release, Unlimited Distribution
PI Meeting Agenda Day 2 – September 30, 2013 Day 2 – Morning Session 8:15am-10:00am – Spatial Situational Awareness (Co-operative Sensing UAV-Ground-Space) • DDDAS for Object Tracking in Complex and Dynamic Environments (DOTCODE) – PI: Anthony Vodacek (RIT) and Team • Dynamic Data Driven Adaptation via Embedded Software Agents for Border Control Scenario – PI: Shashi Phoha (Penn State), and Team • Multiscale Analysis of Multimodal Imagery for Cooperative Sensing – PIs: Erik Blasch, Guna Seetharaman, RI Directorate, AFRL • New Globally Convex Models for Vision Problems using Variational Methods (LRIR) – PI: Guna Sheetharanam, AFRL-RI 10:00am-10:15am – Break 10:15am-11:45am – Spatial Situational Awareness (Co-operative Sensing UAV-Ground-Space) – cont’d • Adaptive Stream Mining: A Novel Dynamic Computing Paradigm for Knowledge Extraction – PI: Shuvra Bhattacharyya (U. Of Maryland) and Team • Stochastic Logical Reasoning for Autonomous Mission Planning – PI: Carlos A. Varela (RPI) • Hybird Systems Modeling and Middleware-enabled DDDAS for Next-generation US Air Force Systems – PI: Aniruddha Gokhale (Vanderbilt U.), and Team (Doug Schmidt) 11:45am-1:00pm --Lunch 9 Distribution A: Approved for Public Release, Unlimited Distribution
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