Handbook of Dynamic Data Driven Applications Systems
Volume 3
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Product details:
- Publisher Springer
- Date of Publication 8 September 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031885730
- Binding Hardback
- No. of pages881 pages
- Size 235x155 mm
- Language English
- Illustrations 56 Illustrations, black & white; 336 Illustrations, color 700
Categories
Short description:
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)
MoreLong description:
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.
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Table of Contents:
Chapter 1 The Dynamic Data Driven Applications Systems (DDDAS) Paradigm Informs Artificial Intelligence towards Digital Science and Engineering.- Chapter 2 Towards Formal Correctness Envelopes for Dynamic Data-Driven Aerospace Systems.- Chapter 3 Dynamic Data Assimilation for Atmospheric Composition: Advances and Perspectives.- Chapter 4 A Model Data Fusion for Statistical Characterization of Constitutive Parameters: Applications to Site Characterization and Seismic Performance Evaluation.- Chapter 5 A Graphical Approach to Modeling Dynamic Data Driven Applications Systems (DDDAS) for Dynamic Node Classification and Link Prediction.- Chapter 6 Uncertainty Analysis of Composite Laminates using Cohesive Layer with Polynomial Chaos and Machine Learning.- Chapter 7 Dynamic Data Driven Applications Systems Analysis of Microtexture Regions in Titanium Alloys.- Chapter 8 Decoupled Data based Control (D2C 2.0).- Chapter 9 A Computational Steering Framework for Large-Scale Composite Structures. Part II: Optimization and Control.- Chapter 10 A novel DDDAS architecture combining advanced sensing and simulation technologies for effective real-time structural health monitoring.- Chapter 11 Systems that Sense and Respond: Modeling, Analysis, and Control of Buildings.- Chapter 12 Deep Learning and Dynamic Mode Decomposition for Characterizing Combustion Instability.- Chapter 13 Reduced-order Modeling of a Nuclear Power Plant for Real-time Monitoring and Control.- Chapter 14 Dynamic Data-driven Estimation of Power System Linear Sensitivity Distribution Factors.- Chapter 15 Intelligent Energy Systems within the DDDAS Framework.- Chapter 16 Self-healing of Distributed Microgrids using DDDAMS.- Chapter 17 Computational and MR-guided Patient-Specific Laser Induced Thermal Therapy of Cancer.- Chapter 18 Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery.- Chapter 19 Human-Allied Learning of Probabilistic Models from Relational Data.- Chapter 20 Info-Symbiotic Systems for Emergencies Governance: Pandemics and Human Security.- Chapter 21 Adversarial Inference: From Inverse Filtering to Inverse Cognitive Radar.- Chapter 22 Distributed Dynamic Data Driven Multi-Threat Tracking.- Chapter 23 A Dynamic Data Driven Approach for Explainable Scene Understanding.- Chapter 24 Advances on Dynamic and Robust Tensor Data Analysis: The Dynamic L1-Tucker Method.- Chapter 25 Implementing a Trajectory Optimization Layer for Persistent Sampling Missions with Soaring.- Chapter 26 Data-driven Routing of Autonomous Vehicles for Distributed Estimation of Spatiotemporal Fields.- Chapter 27 Lane-Based Large-Scale UAS Traffic Management: Contingency Handling.- Chapter 28 Initial Orbit Determination of Resident Space Objects with Ck-networks.- Chapter 29 DDDAS @ 5G and Beyond 5G Networks for Resilient Communications.- Chapter 30 Infrastructures and Microgrid Clusters Dynamic Data-Driven Application Systems for Trust Dynamics.- Chapter 31 Resilient Machine Learning (rML) Ensemble Against Adversarial Machine Learning Attacks to Industrial Control Systems.- Chapter 32 Dynamic Data-Driven Digital Twins for Blockchain Dynamics.- Chapter 33 DDDAS and Security in Distributed Digital Nuclear Systems.- Chapter 34 Dynamic Data Driven Applications Systems (DDDAS) for Cyber Risk Management in Microgrids.- Chapter 35 Dynamic Data Driven Applications Systems (DDDAS) Perspectives and Outlook.
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