Skip to content

To register on our site and for the best user experience, please enable Javascript in your browser using these instructions.

Cart

Register Sign in Wishlist

Unlocking potential with the best learning and research solutions

Dynamic Mode Decomposition

I want this title to be available as an eBook

Dynamic Mode Decomposition
Data-Driven Modeling of Complex Systems

£59.99

Authors:
J. Nathan Kutz, University of Washington
Steven L. Brunton, University of Washington
Bingni W. Brunton, University of Washington
Joshua L. Proctor, Institute for Disease Modeling

Date Published: January 2017
availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
format: Paperback
isbn: 9781611974492

£ 59.99
Paperback

This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.

Unavailable Add to wishlist

Looking for an inspection copy?

This title is not currently available on inspection

Description

About the Authors

Data-driven dynamical systems is a burgeoning field, connecting how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is the first book to address the DMD algorithm and present a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development. By blending theoretical development, example codes, and applications, the theory and its many innovations and uses are showcased. The efficacy of the DMD algorithm is shown through the inclusion of example problems from engineering, physical sciences, and biological sciences, and the authors provide extensive MATLAB® code, data for intuitive examples of key methods, and graphical presentations. This book can therefore be used in courses that integrate data analysis with dynamical systems, and will be a useful resource for engineers and applied mathematicians.
- Highlights the numerous innovations around the DMD algorithm
- Blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses
- Provides data for intuitive examples of key methods, extensive MATLAB® code, and graphical presentations
Read more
Customer reviews

Not yet reviewed
Be the first to review

Log in to review

Review was not posted due to profanity
×

, create a review

(If you're not , sign out)

Please enter the right captcha value

Please enter a star rating.

Your review must be a minimum of 12 words.

How do you rate this item?

Reviews must contain at least 12 words about the product.

×

Product details
- Date Published: January 2017
- format: Paperback
- isbn: 9781611974492
- length: 248 pages
- dimensions: 255 x 177 x 16 mm
- weight: 0.55kg
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
Table of Contents
Preface
Notation
Acronyms
1. Dynamic mode decomposition: an introduction
2. Fluid dynamics
3. Koopman analysis
4. Video processing
5. Multiresolution DMD
6. DMD with control
7. Delay coordinates, ERA, and hidden Markov models
8. Noise and power
9. Sparsity and DMD
10. DMD on nonlinear observables
11. Epidemiology
12. Neuroscience
13. Financial trading
Glossary
Bibliography
Index.
Authors
J. Nathan Kutz, University of Washington
J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics, Adjunct Professor of Physics and Electrical Engineering, and Senior Data Science Fellow with the eScience Institute at the University of Washington.
Steven L. Brunton, University of Washington
Steven L. Brunton is an Assistant Professor of Mechanical Engineering, Adjunct Assistant Professor of Applied Mathematics, and a Data Science Fellow with the eScience Institute at the University of Washington.
Bingni W. Brunton, University of Washington
Bingni W. Brunton is the Washington Research Foundation Innovation Assistant Professor of Biology and a Data Science Fellow with the eScience Institute at the University of Washington.
Joshua L. Proctor, Institute for Disease Modeling
Joshua L. Proctor is an Associate Principal Investigator with the Institute for Disease Modeling, Washington, as well as Affiliate Assistant Professor of Applied Mathematics and Mechanical Engineering at the University of Washington.

Related Books

Data-Driven Fluid Mechanics

Combining First Principles and Machine Learning

Data-Driven Science and Engineering

Machine Learning, Dynamical Systems, and Control

Coarse Graining Turbulence

Modeling and Data-Driven Approaches and their Applications

A First Course in Computational Fluid Dynamics

Viscous Flow

related journals

also by this author

Data-Driven Science and Engineering

Machine Learning, Dynamical Systems, and Control

Data-Driven Science and Engineering

Machine Learning, Dynamical Systems, and Control

Data-Driven Fluid Mechanics

Combining First Principles and Machine Learning

Data-Driven Science and Engineering

Machine Learning, Dynamical Systems, and Control

Save 20% off on your next online purchase with Cambridge Alerts

Receive email alerts on new books, offers and news...

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close