Data-driven science and engineering : machine learning, dynamical systems, and control
By: Brunton, Steven.
Contributor(s): Kutz, J. Nathan [Co-author].
Material type:
TextPublisher: UK CAMBRIDGE UNIVERSITY PRESS 2022Edition: 2nd ed.Description: xxiv, 590p.ISBN: 9781009098489.Subject(s): Sparsity and compressed sensing | Reinforcement Learning | Linear Control TheoryDDC classification: 620.0028 Summary: Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling 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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IMU-MPC Library | Non-fiction | General Stacks | 620.0028 BRU (Browse shelf) | Available | Column - 24 | MPC6651 |
Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling 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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