Higher Order Dynamic Mode Decomposition and Its Applications

Jose Manuel Vega|Soledad Le Clainche

ISBN 10: 0128197439 / ISBN 13: 9780128197431

Published by Elsevier Science, 2020

About this Item

Includes instructions for the implementation of the HODMD, MATLAB codes, and extended discussions of the algorithm Includes descriptions of other order reduction techniques, and compares their strengths and weaknesses Provides exa.

Seller Inventory # 352965288

Bibliographic Details

• Title: Higher Order Dynamic Mode Decomposition and Its Applications
• Publisher: Elsevier Science
• Publication Date: 2020
• Language: English
• ISBN 10: 0128197439
• ISBN 13: 9780128197431
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

• Includes instructions for the implementation of the HODMD, MATLAB codes, and extended discussions of the algorithm
• Includes descriptions of other order reduction techniques, and compares their strengths and weaknesses
• Provides examples of applications involving complex flow fields, in contexts including aerospace engineering, geophysical flows, and wind turbine design

About the Authors

Professor Vega currently holds a Professorship in Applied Mathematics at the School of Aerospace Engineering of the Universidad Politécnica de Madrid (UPM). He received a Master and a PhD, both in Aeronautical Engineering at UPM, and a Master in Mathematics at the Universidad Complutense de Madrid. Along the years, his research has focused on applied mathematics at large, including applications to physics, chemistry, and aerospace and mechanical engineering. The main topics were connected to the analysis of partial differential
equations, nonlinear dynamical systems, pattern formation, water waves, reaction–diffusion problems, interfacial phenomena, and, more recently, reduced order models and data processing tools. The latter two topics are related, precisely, to the content of this book. Specifically, he developed (with Dr. Le Clainche as collaborator) the higher order dynamic mode decomposition method, and also several extensions, including the spatio-temporal Koopman decomposition method. His research activity resulted in the publication of more than one hundred and twenty research papers in first class referred journals, as well as around forty publications resulting from scientific meetings and conferences.

Dr. Soledad Le Clainche holds a Lectureship in Applied Mathematics at the School of Aerospace Engineering of UPM. She received three Masters of Science: in Mechanical Engineering by UPCT, in Aerospace Engineering by UPM, and in Fluid Mechanics by the Von Karman Institute. In 2013 she completed her PhD in Aerospace Engineering at UPM. Her research focuses on computational
fluid dynamics and in the development of novel tools for data analysis enabling the detection of spatio-temporal patterns. More specifically, she has co-developed (with Prof. Vega) the higher order dynamic mode decomposition and variants. Additionally, she has exploited these data-driven tools to develop reduced order models that help to understand the complex physics of dynamical systems. She has also contributed to the fields of flow control, global stability analysis, synthetic jets, analysis of flow structures in complex flows (transitional and turbulent) using data-driven methods, and prediction of temporal patterns using machine learning and soft computing techniques.