Four University of Washington instructors, including three eScience Institute fellows, have co-authored the new book Dynamic Mode Decomposition (DMD): Data-Driven Modeling of Complex Systems. Senior data science fellow J. Nathan Kutz (applied mathematics, physics and electrical engineering) and fellows Steven L. Brunton (mechanical engineering and applied mathematics) and Bingni W. Brunton (biology), along with Joshua L. Proctor (Institute for Disease Modeling, applied mathematics and mechanical engineering) have released the first book to address the DMD algorithm.
The book is intended for applied mathematicians and engineers working in the biological and physical sciences and can be used in classes that integrate data analysis with dynamical systems. Per the book’s description, “data-driven dynamical systems is a burgeoning field — it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known.”
Further, “using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed … DMD is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning.”