DDDAS: The Way Forward

  • Erik Blasch
  • Sai Ravela
  • Alex Aved


This book sought to capture the highlights of DDDAS over the last two decades, with an emphasis on the key areas of development including: theory, modeling, and examples. DDDAS seeks to leverage high-dimensional models to provide data that augments real time estimation, analysis, and control. Many examples were presented that highlight recent approaches, developments, and use of the DDDAS concept towards advancing science through data understanding, analysis, and discovery. The future would further develop these DDDAS concepts towards a better understanding of scientific principles, engineering systems design, and multi-domains applications. DDDAS will leverage and influence such areas as machine learning analytics, multi-domain autonomy, and contextual awareness.


Awareness Autonomy Multi-domain fusion 



The editors, and contributing authors, would like to thank AFOSR for their support and the guidance from Dr. Frederica Darema who developed the DDDAS concept and inspired a generation of researchers. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the United States Air Force.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Erik Blasch
    • 1
  • Sai Ravela
    • 2
  • Alex Aved
    • 3
  1. 1.Air Force Office of Scientific Research, Air Force Research LaboratoryArlingtonUSA
  2. 2.Earth, Atmospheric and Planetary Sciences Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Information DirectorateAir Force Research LaboratoryRomeUSA

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