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Look Inside Data-Driven Science and Engineering

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

CAD$74.95 (P)

  • Publication planned for: April 2019
  • availability: Not yet published - available from April 2019
  • format: Hardback
  • isbn: 9781108422093

CAD$ 74.95 (P)
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About the Authors
  • 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.

    • Provides in-depth examples paired with comprehensive, open-source code
    • Features concise, digestible explanations of complex concepts and their applications
    • Includes extensive online supplements with homeworks, case studies, and supplementary code
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    Reviews & endorsements

    'This is a very timely, comprehensive and well written book in what is now one of the most dynamic and impactful areas of modern applied mathematics. Data science is rapidly taking center stage in our society. The subject cannot be ignored, either by domain scientists or by researchers in applied mathematics who intend to develop algorithms that the community will use. The book by Brunton and Kutz is an excellent text for a beginning graduate student, or even for a more advanced researcher interested in this field. The main theme seems to be applied optimization. The subtopics include dimensional reduction, machine learning, dynamics and control and reduced order methods. These were well chosen and well covered.' Stanley Osher, University of California

    'Professors Kutz and Brunton bring both passion and rigor to this most timely subject matter. Data analytics is the important topic for engineering in the twenty-first century and this book covers the far-reaching subject matter with clarity and code examples. Bravo!' Steve M. Legensky, Founder and General Manager, Intelligent Light

    'Brunton and Kutz provide a lively and comprehensive treatise on machine learning and data mining algorithms as applied to physical systems arising in science and engineering and their control. They provide an abundance of examples and wisdom that will be of great value to students and practitioners alike.' Tim Colonius, California Institute of Technology

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    Product details

    • Publication planned for: April 2019
    • format: Hardback
    • isbn: 9781108422093
    • dimensions: 253 x 177 mm
    • availability: Not yet published - available from April 2019
  • Table of Contents

    Part I. Dimensionality Reduction and Transforms:
    1. Singular value decomposition
    2. Fourier and wavelet transforms
    3. Sparsity and compressed sensing
    Part II. Machine Learning and Data Analysis:
    4. Regression and model selection
    5. Clustering and classification
    6. Neural networks and deep learning
    Part III. Dynamics and Control:
    7. Data-driven dynamical systems
    8. Linear control theory
    9. Balanced models for control
    10. Data-driven control
    Part IV. Reduced-Order Models:
    11. Reduced-order models (ROMs)
    12. Interpolation for parametric ROMs.

  • Resources for

    Data-Driven Science and Engineering

    Steven L. Brunton, J. Nathan Kutz

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  • Authors

    Steven L. Brunton, University of Washington
    Steven L. Brunton is Associate Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Associate Professor of Applied Mathematics and a Data-Science Fellow at the eScience Institute. His research applies data science and machine learning for dynamical systems and control to fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He has co-authored two textbooks, received the Army and Air Force Young Investigator awards, and was awarded the University of Washington College of Education teaching 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 served as department chair until 2015. He is also Adjunct Professor of Electrical Engineering and Physics and a Senior Data-Science Fellow at the eScience Institute. His research interests are in complex systems and data analysis where machine learning can be integrated with dynamical systems and control for a diverse set of applications. He is an author of two textbooks and has received the Applied Mathematics Boeing Award of Excellence in Teaching and an NSF CAREER award.

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