Data-driven science and engineering: machine learning, dynamical systems, and control (brunton, steven l. and kutz, j. nathan; 2020) [bookshelf]

Abstract

This book is about the growing intersection of data-driven methods, applied optimization, and the classical fields of engineering mathematics and mathematical physics. We have been developing this material over a number of years, primarily to educate our advanced undergrad and beginning graduate students from engineering and physical science departments. Typically, such students have backgrounds in linear algebra, differential equations, and scientific computing, with engineers often having some exposure to control theory and/or partial differential equations