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Higher Order Dynamic Mode Decomposition and Its Applications
Provides detailed background theory plus fully explained applications from a range of industrial contexts to help readers understand and use this innovative algorithm
Jose Manuel Vega (Author), Soledad Le Clainche (Author)
9780128197431, Elsevier Science
Paperback, published 22 September 2020
320 pages
22.9 x 15.1 x 2.1 cm, 0.52 kg
Higher Order Dynamic Mode Decomposition and Its Applications provides detailed background theory, as well as several fully explained applications from a range of industrial contexts to help readers understand and use this innovative algorithm. Data-driven modelling of complex systems is a rapidly evolving field, which has applications in domains including engineering, medical, biological, and physical sciences, where it is providing ground-breaking insights into complex systems that exhibit rich multi-scale phenomena in both time and space. Starting with an introductory summary of established order reduction techniques like POD, DEIM, Koopman, and DMD, this book proceeds to provide a detailed explanation of higher order DMD, and to explain its advantages over other methods. Technical details of how the HODMD can be applied to a range of industrial problems will help the reader decide how to use the method in the most appropriate way, along with example MATLAB codes and advice on how to analyse and present results.
1. General introduction and scope of the book2. Higher order dynamic mode decomposition3. HODMD applications to the analysis of flight tests and magnetic resonance4. Spatio-temporal Koopman decomposition5. Application of HODMD and STKD to some pattern forming systems6. Applications of HODMD and STKD in fluid dynamics7. Applications of HODMD and STKD in the wind industry8. HODMD and STKD as data driven reduced order models9. Conclusions
Subject Areas: Fluid mechanics [PHDF]
