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Handbook of Dynamic Data Driven Applications Systems

Volume 2

  • Expands the scope of the methods and the application areas presented in the first volume

  • In-depth discussions reflecting the adoption of DDDAS paradigm from leading experts in various domain

  • Includes examples for new and advanced methods in command and control, swarm analysis, and structural health monitoring

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Table of contents (34 chapters)

  1. Front Matter

    Pages i-x
  2. The Dynamic Data Driven Applications Systems (DDDAS) Paradigm and Emerging Directions

    • Frederica Darema, Erik P. Blasch, Sai Ravela, Alex J. Aved
    Pages 1-51
  3. Fundamentals Aware: Theory/Foundational Methods

    1. Front Matter

      Pages 53-53
    2. Dynamic Data-Driven Applications Systems and Information-Inference Couplings

      • Sai Ravela, Erik P. Blasch, Frederica Darema
      Pages 55-70
  4. Materials Systems

    1. Front Matter

      Pages 121-121
    2. Equation-Free Computations as DDDAS Protocols for Bifurcation Studies: A Granular Chain Example

      • M. O. Williams, Y. M. Psarellis, D. Pozharskiy, C. Chong, F. Li, J. Yang et al.
      Pages 123-146
  5. Structural Systems - Structural – Infrastructures

    1. Front Matter

      Pages 193-193
    2. Bayesian Computational Sensor Networks: Small-Scale Structural Health Monitoring

      • Wenyi Wang, Anshul Joshi, Nishith Tirpankar, Philip Erickson, Michael Cline, Palani Thagaraj et al.
      Pages 229-248
    3. Dynamic Data-Driven Sensor Tasking with Applications in Space and Aerospace Systems

      • Nagavenkat Adurthi, Puneet Singla, Manoranjan Majji
      Pages 249-283
  6. Energy Systems – Energy Production and Distribution

    1. Front Matter

      Pages 285-285
    2. Dynamic Data-Driven Application Systems for Reservoir Simulation-Based Optimization: Lessons Learned and Future Trends

      • M. Parashar, Tahsin Kurc, H. Klie, M. F. Wheeler, Joel H. Saltz, M. Jammoul et al.
      Pages 287-330
    3. DDDAS Within the Oil and Gas Industry

      • Shiyan Wang, Nate Schultheiss, Sangtae Kim
      Pages 331-351
  7. Environmental Systems – Conditions Assessment

    1. Front Matter

      Pages 375-375
    2. Toward Dynamic Data-Driven Systems for Rapid Adaptive Interdisciplinary Ocean Forecasting

      • N. M. Patrikalakis, P. F. J. Lermusiaux, C. Evangelinos, J. J. McCarthy, A. R. Robinson, H. Schmidt et al.
      Pages 377-395

About this book

This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing  DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. 

As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present.  Here, the theory and application content are considered for:

  • Foundational Methods
  • Materials Systems
  • Structural Systems
  • Energy Systems
  • Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires
  • Surveillance Systems
  • Space Awareness Systems
  • Healthcare Systems
  • Decision Support Systems
  • Cyber Security Systems
  • Design of Computer Systems 
The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities;  address challenges that ML-alone does  not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).


  • Controls
  • Instrumentation
  • Big Data
  • Dynamic Systems
  • High performance computing
  • InfoSymbiotics
  • Cyber physical systems
  • UAVs
  • data fusion
  • feature fusion
  • decision fusion
  • information fusion
  • Modeling and Analysis
  • Environmental Analysis
  • Environmental Modeling
  • Statistical modeling
  • data assimilation
  • Autonomy
  • Vehicular Systems



“DDDAS has proven to be a transformative technology as data has become the third part of the triad, with theory and computation as the other two parts. Initially DDDAS was part of control theory but as data have become ubiquitous there has been a paradigm shift, initiated by DDDAS, from simulation to effectively using data for prediction.” (John Cherniavsky , (retired, as Division Director, NSF - US National Science Foundation))

“The DDDAS paradigm integrates and enhances the deepest, most well-grounded foundations and tools - both from expert based methods and from learning-based methods, building well beyond more popular and limited forms of AI. This book provides a unique breadth and depth of scope across many science and technology fields, showing how DDDAS is an overarching concept that connects and unifies the wide diversity across these fields.” (Paul Werbos, (retired, as Program Director, NSF - US National Science Foundation))     

Editors and Affiliations

  • InfoSymbiotic Systems Society, Bethesda, USA

    Frederica Darema

  • MOVEJ Analytics, Dayton, USA

    Erik P. Blasch

  • Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, USA

    Sai Ravela

  • Information Directorate, Air Force Laboratory, Rome, USA

    Alex J. Aved

About the editors

Frederica Darema, PhD, is President and CEO of the InfoSymbiotic Systems Society. She has retired as Senior Executive Service (SES) member and Director of the Air Force Office of Scientific Research, Arlington, Virginia, where she led the entire basic research investment for the AF, and she concurrently served as Research Director in the Air Force’s Chief Data Office, and as Associate Deputy Assistant Secretary at the Air Force Office for Science, Technology and Engineering. Prior career history includes research staff positions at the University of Pittsburgh, Brookhaven National Laboratory, and Schlumberger-Doll; management and executive-level positions at the T. J. Watson IBM Research Center and the IBM Corporate Strategy Group, the National Science Foundation, and the Defense Advanced Research Projects Agency; and director of the AFOSR Directorate for Information, Math, and Life Sciences. Dr. Darema, PhD in nuclear physics, is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), among other professional recognitions. In 1983, she pioneered the SPMD computational model which is the predominant model for parallel (super)computing; and in 1980, she pioneered the DDDAS paradigm, and since 2000 she has organized and led research initiatives, programs, workshops, conferences (including the biannual DDDAS/InfoSymbiotic Systems Conference series, co-led with co-editors: Blasch, Ravela, and Aved; 2016-present), and other forums to foster and promote DDDAS-based science and technology advances.

Erik P. Blasch, PhD, is a Program Officer with the Air Force Office of Scientific Research. His focus areas are in multi-domain (space, air, ground) data fusion, target tracking, pattern recognition, and robotics. He has authored 750+ scientific papers, 22 patents, 30 tutorials, and 5 books. Recognitions include the Military Sensing Society Mignogna leadership in data fusion award, IEEE Aerospace and Electronics Systems Society Mimno best magazine paper award, IEEE Russ bioengineering award, and founding member of the International Society of Information Fusion (ISIF). Previous appointments include adjunct associate professor at Wright State University, exchange scientist at Defense Research and Development Canada, and officer in the Air Force Research Laboratory. Dr. Blasch is an associate fellow of AIAA, fellow of SPIE, and fellow of IEEE.

Sai Ravela, PhD, directs the Earth Signals and Systems Group (ESSG) in the Earth Atmospheric and Planetary Sciences (EAPS) Department at the Massachusetts
Institute of Technology. In addition, he is presently an Engineering Fellow at Cytonome conducting Cell Imaging & Biofluidic Control R&D, he is a co-Founder of WindrisktechLLC, quantifying Hurricane-induced Risk in a changing climate. Dr. Ravela’s primary interests are in statistical pattern recognition, stochastic nonlinear systems science, and computational intelligence, with application to earth, planets, climate, and life. Dr. Ravela introduced new methods for coherent fluid dynamical regimes, applying them to DDDAS-based observing systems of localized atmospheric phenomena, laboratory studies, and wildlife. He has advanced learning-based approaches to DDDAS, and introduced the ensemble-based informative approach for DDDASbased learning and hybrid stochastic systems. Dr. Ravela is the recipient of the MIT 2016 Infinite Kilometer award for exceptional research and mentorship. Dr. Ravela organized the Dynamic Data Driven Environmental Systems Science Conference (DyDESS 2014, Cambridge), and has co-organized all DDDAS conferences (2016-2022).

Alex J. Aved, PhD, is a Senior Researcher with the Air Force Research Laboratory, Information Directorate, Rome, NY, USA. His research interests include multimedia databases, stream processing (via CPU, GPU, or coprocessor), and dynamically executing models with feedback loops incorporating measurement and error data to improve the accuracy of the model. He has published over 50 papers and given numerous invited lectures. Previously, he was a programmer at the University of Central Florida and database administrator and programmer at Anderson University.

Bibliographic Information

Buy it now

Buying options

eBook USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access