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.
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.
Erik P. Blasch, Ph.D./MBA, is a research engineer focused on information fusion performance evaluation, multi-modal image fusion, multi-domain avionics, space situational awareness, and human-machine integration He received his B.S. in Mechanical Engineering from the Massachusetts Institute of Technology and Ph.D. in Electrical Eng. from Wright State University in addition to numerous Masters Degrees in Mech. Eng., Ind. Eng., Health Science, Elect. Eng., Economics, Psychology, Medicine, and Military Studies. His affiliations include adjunct associate professor, President of professional societies, member of the International Society of Information Fusion (ISIF) Evaluation of Techniques for Uncertainty Representation Working Group (ETURWG), and member of the IEEE Avionics Systems Technical Panel. He has compiled 16 books, 68 patents, 182+ journal papers, and 500+ peer-reviewed publications. His contributions include: physics-based and human-derived information fusion (PHIF), the data fusion information group (DFIG) model, confusion-matrix data fusion, game-theoretic space situational awareness, evidential reasoning simultaneous tracking and identification (STID) filtering, and the multisource AI scorecard table (MAST). He is the recipient of 21 performance medals, 8 named awards, as well as Fellow of AIAA (astronautics), IEEE (electrical), MSS (sensing), RAeS (aerospace), and SPIE (optical) societies.
Frederica Darema, PhD, is the President and CEO of the InfoSymbiotic Systems Society. Retired (2019) as Senior Executive Service (SES) member and as Director of the Air Force Office of Scientific Research, in Arlington, Virginia, where she led the entire basic research S&T investments for the AF; also served in the Office of the Secretary of the Air Force, as Research Director in the Air Forces Chief Data Office, and as Associate Deputy Assistant Secretary in 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: T.J.Watson IBM Research Center and IBM Corporate Strategy Group; National Science Foundation and Defense Advanced Research Projects Agency; Director of the AFOSR Directorate for Information, Math, and Life Sciences. Dr. Darema received her PhD (University of California at Davis) in Nuclear Physics, and as Fulbright Scholar and Distinguished Scholar. She is IEEE Life Fellow; WAAS Fellow (World Academy of Arts and Sciences), among other distinctions. Made seminal contributions in computational sciences and the supercomputing field (pioneered: the SPMD computational model; and the DDDAS paradigm). She serves on university Advisory Boards and governmental research review panels; and she is co-Editor of the DDDAS Handbooks and Conferences Proceedings.
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, and emerging integrated circuit architectures), and dynamically executing models with feedback loops incorporating measurement and error data to improve the accuracy of the model. He has published over 100 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.
