Handbook of dynamic data driven applications systems. Volume 2 / Frederica Darema, Erik P. Blasch, Sai Ravela, Alex J. Aved, editors.
Darema, Frederica.; Blasch, Erik.; Ravela, Sai.; Aved, Alex J.
2023
QA76.9.C65
Available Online

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Title Handbook of dynamic data driven applications systems. Volume 2 / Frederica Darema, Erik P. Blasch, Sai Ravela, Alex J. Aved, editors.
ISBN 9783031279867 (electronic bk.)
3031279867 (electronic bk.)
3031279859
9783031279850
DOI https://doi.org/10.1007/978-3-031-27986-7
Imprint Cham : Springer, 2023.
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-031-27986-7 doi
Call Number QA76.9.C65
Dewey Decimal Classification 003.3
Summary 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 through the examples and case studies presented, either within their own field or other fields of study. 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 .
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed September 27, 2023).
Added Author Darema, Frederica.
Blasch, Erik.
Ravela, Sai.
Aved, Alex J.
Available in Other Form Print version: 9783031279850
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Record Appears in Online Resources > Ebooks
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Intro
Contents
About the Editors
1 The Dynamic Data Driven Applications Systems (DDDAS) Paradigm and Emerging Directions
1.1 Introduction
1.2 Overview of the DDDAS Paradigm
1.2.1 The DDDAS Paradigm: Definition and Features
1.2.2 DDDAS Background
1.2.3 State Estimation and Data Assimilation
1.2.4 DDDAS and Adaptive State Estimation
1.2.5 Does DDDAS Use Feedback Control?
1.3 DDDAS Methods
1.4 Historical Perspective of DDDAS Research and Breakthroughs
1.4.1 Theory: Modeling and Analysis
1.4.2 Methods: Domain Applications

1.4.3 Analysis and Design: Systems and Architectures
1.5 Emerging Domains of DDDAS Impact
1.5.1 DDDAS and Data Assimilation and Digital Twins
1.5.2 DDDAS and Test and Evaluation
1.5.3 DDDAS and 5G and Beyond Networks
1.6 Differences and Advantages of DDDAS Vis-a-Vis "AI" Methods
1.6.1 DDDAS and AI Techniques
1.6.2 DDDAS Inspires NN Methods
1.6.3 Differences and Advantages of DDDAS Versus CPS
1.7 Book Overview
1.8 Summary
References
Part I Fundamentals Aware: Theory/Foundational Methods

2 Dynamic Data-Driven Applications Systems and Information-Inference Couplings
2.1 Introduction
2.2 System Dynamics and Optimization
2.2.1 DDDAS and Sparsity: An Example
2.3 DDDAS: From Inference to Information and Back
2.3.1 Human-Machine Teaming: An Example
2.4 Emerging Opportunities
2.5 Conclusions
References
3 Polynomial Chaos Expansion-Based Nonlinear Filtering for Dynamic State Estimation
3.1 Introduction
3.2 Literature Review
3.3 Uncertainty Propagation by Using Polynomial Chaos Expansion
3.4 Polynomial Chaos-Based Ensemble Square Root Filter

3.4.1 Ensemble Square Root Filter
3.4.2 Polynomial Chaos-Based Ensemble Square Root Filter
3.5 Existing Nonlinear Filters and Complexity Analysis
3.6 Application to Ballistic Trajectory Estimation Problems
3.7 Conclusion
References
4 Measure-Invariant Symbolic Systems for Pattern Recognition and Anomaly Detection
4.1 Introduction
4.2 Background
4.3 Symbolic Time Series Analysis
4.3.1 Measure-Invariant Symbolic Systems
4.3.2 Probabilistic Finite State Automata
4.3.3 D-Markov Machines
4.3.4 Anomaly Detection in the STSA Setting
4.4 Technical Approach

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