This book is an accessible and comprehensive introduction to the field of data-driven science and engineering. It is unique in the sense that it brings together interdisciplinary concepts from machine learning, dynamical systems, and feedback control and applies them to physical systems arising in science and engineering. The book provides a broad overview of these concepts and develops tools for data-driven modeling, prediction, and control. Overall, it provides a perfect starting point for an aspiring graduate student or researcher in this field. It can also be used as a text for an advanced undergraduate or graduate course on data-driven model reduction and control. A wealth of accompanying online material (such as Matlab/Python code and a variety of YouTube video lectures) makes the book very suitable for self-study

中文翻译：

---

数据驱动的科学与工程：机器学习、动力系统和控制 (brunton, steven l. and kutz, j. nathan; 2020) [书架]

本书是对数据驱动科学与工程领域的通俗易懂的全面介绍。它的独特之处在于它将来自机器学习、动力系统和反馈控制的跨学科概念结合在一起，并将它们应用于科学和工程中出现的物理系统。本书对这些概念进行了广泛的概述，并开发了用于数据驱动建模、预测和控制的工具。总的来说，它为该领域有抱负的研究生或研究人员提供了一个完美的起点。它也可以用作有关数据驱动模型简化和控制的高级本科或研究生课程的教材。丰富的随附在线资料（如Matlab/Python代码和各种YouTube视频讲座）使本书非常适合自学