Welcome to the DDDAS2020 Conference Conference Co-Chairs: Dr. - PowerPoint PPT Presentation

DDDAS – Dynamic Data Driven Applications Systems Past, Present, Future DDDAS2020 & Beyond Frederica Darema, Ph.D., LF/IEEE (retired) Director – Senior Executive (SES) DDDAS2020 Conference October 2-4, 2020 www.1dddas.org 1

Welcome to the DDDAS2020 Conference Conference Co-Chairs: Dr. Frederica Darema and Dr. Erik Blasch Program Co-Chairs: Dr. Alex Aved, Prof Sai Ravela, Dr. Murali Rangaswamy Program Committee: • Robert Bohn (NIST) • Richard Linares (MIT) • Newton Campbell (NASA and SAIC) • Dimitri Metaxas (Rutgers Univ) • Nurcin Celik (U of Miami) • Jose Moreira (IBM) • Ewa Deelman (USC/ISI) • Chiwoo Park (FSU) • Salim Hariri (U of Arizona) • Sonia Sachs (DOE) • Thomas Henderson (U of Utah) • Ludmilla Werbos (U of Memphis) • Artem Korobenko (U of Calgary) • Themistoklis Sapsis (MIT) • Fotis Kopsaftopoulos (RPI) • Amit Surana (UTRC) Agenda: Keynotes, Plenaries, Posters, Panels 2

DDDAS Initiative Launched (March 2000) DDDAS (Dynamic Data Driven Applications Systems) DEFINTION of DDDAS (2000/NSF Workshop) InfoSymbiotic Systems DDDAS: ability to dynamically incorporate additional data into an executing application, and in reverse, ability of an application to dynamically steer the measurement process Challenges : Application Modeling/Simulations Methods Algorithmic Stability Measurement/Instrumentation Methods Computing Systems Software Support Instrumentation -> Synergistic, Multidisciplinary Research Observation/Actuation Dynamic Capabilities : Feedback & Control more accurate understanding/prediction systems characteristics/behaviors Loop  speeding-up simulation/modeling, by replacing computation with instrumentation data in specific parts of the phase-space of the model  improve accuracy of the model by augmenting the model with actual data to improve analysis/prediction capabilities of application models  dynamically manage/schedule/architect heterogeneous resources (2000/NSF Workshop)  networks of heterogeneous sensors or controllers  detect and mitigate sensor failures Dynamic Integration of Computation & Measurements/Data  enable decision-support capabilities with simulation/modeling accuracy Unification of Computing Platforms & Sensors/Instruments (from the High-End to the Real-Time, to the PDA) 3 DDDAS – > architecting & adaptive management of sensor systems

DDDAS Development Large-Scale-Big-Data Large-Scale-Big-Computing QVO VADIMUS AFOSR-NSF Program AFOSR NSF Program Workshop 2016 DDDAS AFOSR-NSF NSF DDDAS 2014 Workshop Workshop NSF DDDAS Program InfoSymbiotic 2011 DDDAS 2010 DDDAS term coined NSF/ITR 2006 ( symbiotic systems ) 2005 NSF/NGS 2000 2003 IBM; UTC; DARPA Program ASME Performance Gedanken Engineering Idea 1999 Laboratory 1998 spawned 1996 1980 1984-86 4

Example Highlights of DDDAS Impact • 2000 - Kelvin Droegemeier – Adverse Weather /Tornadic activity – LEAD project: Users INTERACTING with Weather March 2000 Fort Worth Interaction Level II: Tools and People Driving Tornadic Storm Local TV Station Radar Observing Systems – Dynamic Adaptation (Slide – Courtesy K. K. Droegemeier) NWS National Static Observations & Grids Tornado Virtual/Digital Resources and Services Users Mesoscale ADaM ADAS Experimental Dynamic Tools Weather Remote Physical (Grid) Observations Resources Local Physical Resources “ Sensor Networks & Computer Networks ” Local Observati on s NEXRAD CASA Slide courtesy Droegemeier  2010 - Tinsley Oden – showed predicting on-set in materials damage before visible  2011 – Nurcin Celik - Powergrids Real-time Decision Support - Renewable Resources – multiple consumer classes&priorities  2012 – Karen Willcox – Real-Time Decision Support for aerial platforms - Structural Health Monitoring and Mission Planning Potential examples (DDDAS-based solutions): • Tree-tomography • Future aircraft designs (Dutch V-shaped – DelftU -KLM) (WashDC Sculpture Garden) 5

Examples of Areas of DDDAS Impact from the nano-scale, to the tera-scale, to the extraterra-scale • Materials – Fundaments & Design DDDAS/InfoSymbiotics drives: • Structural Health Monitoring • Foundational methods • Advanced Manufacturing  Filtering, Estimation, • Smart Civil Infrastructures  Machine Learning • Transportation  Uncertainty Quantification • Power-grids • Applications approaches • Water Distribution  systems-of-systems • Smart Cities  representation models • Ecological Systems  network control  sensor management • Smart Agriculture • Atmospheric Weather DDDAS has influenced extensions: • Adverse events  Data Assimilation • Hurricanes  Digital Twin • Tornadoes  GANs • Earthquakes • Environmental Disasters Recent/emerging ML algorithms • Wildfires apply and/or adopt the essence the • Oil Spills DDDAS paradigm • Earthquakes  Informative Sensing, Estimation, Planning • Space Weather  Targeted Observation, Active Learning • Land, Air, Space  Reinforcement Learning RelevanceFeedback • Emergency Response  Stochastic Modeling, Feature Selection AFOSR-NSF 2010 Report (www.1dddas.org) • Resource Planning  Recommender Systems, etc Other initiatives, such as: • Supply-Chain Logistics Cyber-physical Systems (NSF 2006) • Model-based Real-time Decision Support can benefit from the more comprehensive -> Autonomic Systems approaches of the DDDAS paradigm 6

QUO VADIMUS Moving into the future there is a confluence of needs and technological advances: Increasingly we deal with systems-of-systems & systems/environments that are complex | heterogeneous | multimodal | multiscale | dynamic Need to understand characteristics/behaviors: design – operation – evolution – interoperability – maintenance -- lifecycle Ad-hoc methods are not enough – need modeling not only for design but entire file-cycle Data alone is not enough Data is not the 4 th paradigm… - Data is the primordial paradigm Data Analytics is not enough - We need Systems Analytics ML alone is not enough -> …. Models Data …. DDDAS/InfoSymbiotics has shown:  comprehensive, principle-based models rendered more efficient and accurate  understanding, prediction, and real-time decision support with the accuracy of full-scale models  dynamic and adaptive coordination of heterogeneous resources abilities, and ensuring fault tolerance DDDAS/InfoSymbiotics - timely now more than ever:  increased emphasis in complex systems multi-scale/multi-modal modeling/algorithmic methods  ubiquitous sensing, networks of heterogeneous collections of sensors/controllers  large-scale-big-computing – large-scale-big-data;  increased computation and communication capabilities  enable and exploit these capabilities Some New Opportunities Areas: Test&Evaluation:  embedded sensors&actuators (physical and software); additive manufacturing  DDDAS-based adaptivity to improve performance, system evolution adaptivity – interoperability - maintenance;  No longer limited to the design (“breadboarding”) cycle – T&E become a lifecycle process 5G&Beyond: 7  DDDAS-based methods for adaptive coordination of heterogeneous resources; optimization of performance, energy, QoS