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Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen…
Offers first text in data science where data methods for scientific discovery are highlighted, aimed at advanced undergraduates, graduate students and researchers
Highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, e.g. turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy
Supplementary material – including lecture videos for every section, homework for all chapters, data, full codes in Python, MATLAB®, Julia, and R, and additional case studies – can be found on databookuw.com
Prerequisites include calculus, linear algebra 1, and basic computational proficiency in either Python or MATLAB
Suitable for applied data science courses, including: Applied Machine Learning; Beginning Scientific Computing; Computational Methods for Data Analysis; Applied Linear Algebra; Control Theory; Data-Driven Dynamical Systems; Machine Learning Control; Reduced Order Modeling
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Authors
Steven L. Brunton,University of Washington
Steven L. Brunton is the James B. Morrison Professor of Mechanical Engineering at the University of Washington and Associate Director of the NSF AI Institute in Dynamic Systems. He is also Adjunct Professor of Applied Mathematics and Computer Science and a Data-Science Fellow at the eScience Institute. His research merges data science and machine learning with dynamical systems and control, with applications in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He is an author of three textbooks, and received the UW College of Engineering Teaching award, the Army and Air Force Young Investigator Program (YIP) awards, and the Presidential Early Career Award for Scientists and Engineers (PECASE) award.
J. Nathan Kutz,University of Washington
J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington and Director of the NSF AI Institute in Dynamic Systems. He is also Adjunct Professor of Electrical and Computer Engineering, Mechanical Engineering, and Physics and Senior Data-Science Fellow at the eScience Institute. His research interests lie at the intersection of dynamical systems and machine learning. He is an author of three textbooks and has received the Applied Mathematics Boeing Award of Excellence in Teaching and an NSF CAREER award.