Higher Order Dynamic Mode Decomposition and Its Applications

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.

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