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Data-Driven Methods for Dynamic Systems - Jason J. Bramburger

Data-Driven Methods for Dynamic Systems

Buch | Softcover
169 Seiten
2024
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-815-5 (ISBN)
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Modern computational tools merge centuries-old mathematical techniques with innovative approaches to analyze dynamic, time-dependent systems. Example-driven methods and online code support empower graduate students and researchers to explore dynamic data modeling, differential equations, and data science effectively.
As experimental data sets have grown and computational power has increased, new tools have been developed that have the power to model new systems and fundamentally alter how current systems are analyzed. This book brings together modern computational tools to provide an accurate understanding of dynamic data. The techniques build on pencil-and-paper mathematical techniques that go back decades and sometimes even centuries. The result is an introduction to state-of-the-art methods that complement, rather than replace, traditional analysis of time-dependent systems.

Data-Driven Methods for Dynamic Systems provides readers with methods not found in other texts as well as novel ones developed just for this book; an example-driven presentation that provides background material and descriptions of methods without getting bogged down in technicalities; and examples that demonstrate the applicability of a method and introduce the features and drawbacks of their application. The online supplementary material includes a code repository that can be used to reproduce every example and that can be repurposed to fit a variety of applications not found in the book.

This book is intended as an introduction to the field of data-driven methods for graduate students. It will also be of interest to researchers who want to familiarize themselves with the discipline. It can be used in courses on dynamical systems, differential equations, and data science.

Jason Bramburger is an assistant professor of mathematics at Concordia University. Previously, he was an NSERC postdoctoral fellow at Brown University, a PIMS postdoctoral fellow at the University of Victoria, and a member of the Brunton–Kutz research group at the University of Washington. In 2021 he was named a fellow of the Institute of Advanced Study in Guildford, UK. His mathematical expertise is in dynamical systems, which he applies broadly to problems in pattern formation, wave propagation, spatiotemporal chaos, and data science. Jason has a passion for outreach and effective communication, as demonstrated by expository articles and an active YouTube channel with course lectures on differential equations, mathematical modeling, and dynamical systems.

Erscheinungsdatum
Verlagsort New York
Sprache englisch
Themenwelt Informatik Theorie / Studium Algorithmen
Mathematik / Informatik Mathematik Angewandte Mathematik
ISBN-10 1-61197-815-7 / 1611978157
ISBN-13 978-1-61197-815-5 / 9781611978155
Zustand Neuware
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