Handbook of dynamic data driven applications systems. Volume 2

Handbook of dynamic data driven applications systems. Volume 2 / Frederica Darema, Erik P. Blasch, Sai Ravela, Alex J. Aved, editors.

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...

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Bibliographic Details
Other Authors: Darema, Frederica, Blasch, Erik, Ravela, Sai, Aved, Alex J.
Format: eBook
Language:English
Published: Cham : Springer, 2023.
Subjects:
Computer simulation.
System theory.
Computers, Special purpose.
simulation.
Electronic books.
handbooks.
Handbooks and manuals.
Guides et manuels.
Online Access:Click for online access
Table of Contents:
  • 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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