数据科学
应用科学
计算机科学
机器人学
科学与工程
航程(航空)
研究生
工程类
自治
气候科学
数据驱动
复杂系统
人工智能
工程伦理学
管理科学
机器人
航空航天工程
心理学
生态学
教育学
气候变化
政治学
法学
生物
作者
Steven L. Brunton,J. Nathan Kutz
标识
DOI:10.1017/9781108380690
摘要
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
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