Handbook of Dynamic Data Driven Applications Systems: Volume 3

This authoritative reference on the Dynamic Data Driven Applications Systems paradigm benefits science and technology researchers and practitioners developing DDDAS-based technologies. This Third Volume in the handbook series offers 35 chapters contributed by leading experts, organized in nine thematic areas spanning natural, engineered, or societal systems to enable an accurate understanding, analysis, and control of corresponding complex systems. The work presented shows how DDDAS unifies the computational and instrumentation aspects of an application system, extends computing notions to span from the high-end to edge computing and real-time data acquisition and control, adaptively manages the operation of complex, distributed, multimodal systems through high-dimensional, systems-cognizant model-coordination and multi-objective optimization of resources and service delivery; supports methodologies for achieving autonomic and enhanced AI capabilities; and provides a foundational concept for Predictive Digital Twins and Dynamic Digital Twins. Advance Praise: DDDAS opened the way to combine real-time data sensing with simulation modeling, thereby creating a way to produce fast predictions with unprecedented accuracy. The adoption of the technique has impacted a broad spectrum of applications. The March 2023 US National Academies (NASEM) Report on “Foundational Research Gaps and Future Directions for Digital Twins" identifies DDDAS as an early concept that "has all of the elements of a digital twin, including the physical, the virtual, and the two-way interaction via a feedback loop". The Report also characterizes Digital Twins as holding "immense promise in accelerating scientific discovery and revolutionizing industries." The chapters in this volume give insight to that cited power of Digital Twins through the accomplishments reported for DDDAS, allowing S&T experts to dig deeply in specific areas, as well as providing hints for new ways to deploy it. ­ —Harold Stone, Ph.D. (Fellow Emeritus, NEC Research Institute) The development of trusted Dynamic Data-Driven Applications Systems will enable leaps forward in scientific discovery, from autonomous robotic exploration of planetary surfaces to unprecedented skill in weather and climate prediction. But the applications will go well beyond academia: digital twins, enabled by DDDAS, will help describe the interaction of humans with the environment and will be critical tools for policymakers and urban planners. —Michael Seablom (Associate Director for Technology in the Earth Science Division and Head of the Earth Science Technology Office (ESTO), NASA Headquarters) This handbook is an authoritative reference on the Dynamic Data Driven Applications Systems paradigm, pioneered by Dr. Darema, and contains DDDAS-based work conducted by contributing co-authors in numerous areas, to benefit science and technology researchers and practitioners developing DDDAS-based technologies. Beginning with general concepts and history of the DDDAS paradigm in the introductory chapter, this Third Volume in the series contains 33 chapters contributed by leading experts and is organized in nine thematic areas, spanning natural, engineered, or societal systems to enable an accurate understanding, analysis, and control of corresponding complex systems. The thematic areas cover fundamental and foundational methods; materials, structural, and energy systems; healthcare and situation awareness systems; air and space, communications, and cyber systems. The work presented shows how DDDAS unifies the computational and instrumentation aspects of an application system, extends computing notions to span from the high-end to the edge computing and real-time data acquisition and control, adaptively manages the operation of complex, distributed, multimodal systems through high-dimensional, systems-cognizant model-coordination and multi-objective optimization of resources and service delivery; supports methodologies for achieving autonomic and enhanced AI capabilities; DDDAS is foundational concept for Predictive Digital Twins and Dynamic Digital Twins. The authorsexplain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination.