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> Data-Driven Science and Engineering

Data-Driven Science and Engineering Machine Learning, Dynamical Systems, and Control


Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
Published 2022


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…

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Key features

  • 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
  • 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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