book chapterCambridge University Press eBooksMay 5, 2022Closed access

Physics-Informed Machine Learning

SLSteven L. BruntonJNJ. Nathan Kutz

University of Washington

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Abstract

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 for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to…

Citation impact

271
total citations
FWCI
89.34
Percentile
100%
References
0
Citations per year

Authors

2
  • SL
    Steven L. BruntonCorresponding

    University of Washington

  • JN
    J. Nathan Kutz

    University of Washington

Topics & keywords

Keywords
  • Python (programming language)
  • Computer science
  • Generality
  • MATLAB
  • Artificial intelligence
  • Toolbox
  • Bridging (networking)
  • Graduate students
UN Sustainable Development Goals
  • Quality Education
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