Authors:
- Combines qualitative and quantitative modeling and efficient computational methods
- Presents topics from nonlinear dynamics, stochastic modeling, numerical algorithms, and real applications
- Includes MATLAB® codes for the provided examples to help readers better understand and apply the concepts
Part of the book series: Synthesis Lectures on Mathematics & Statistics (SLMS)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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Department of Mathematics, University of Wisconsin–Madison, Madison, USA
Nan Chen
About the author
Bibliographic Information
Book Title: Stochastic Methods for Modeling and Predicting Complex Dynamical Systems
Book Subtitle: Uncertainty Quantification, State Estimation, and Reduced-Order Models
Authors: Nan Chen
Series Title: Synthesis Lectures on Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-031-22249-8
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 12
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-22248-1Published: 14 March 2023
Softcover ISBN: 978-3-031-22251-1Published: 14 March 2024
eBook ISBN: 978-3-031-22249-8Published: 13 March 2023
Series ISSN: 1938-1743
Series E-ISSN: 1938-1751
Edition Number: 1
Number of Pages: XVI, 199
Number of Illustrations: 1 b/w illustrations, 36 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Complex Systems, Applications of Mathematics, Data Structures and Information Theory, Artificial Intelligence, Theory of Computation