Data-Driven Science and Engineering Machine Learning, Dynamical Systems, and Control A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. Steven L. Brunton (Author), J. Nathan Kutz (Author) 9781009098489, Cambridge University Press Hardback, published 5 May 2022 614 pages25.9 x 18.3 x 3.1 cm, 1.39 kg 'This is one of the best textbooks in the field. I am adopting it along with a data-driven fluid mechanics text.' Kianoosh Yousefi, University of Texas at Dallas 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 advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com. 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 Part IV. Advanced Data-Driven Modeling and Control: 10. Data-Driven Control 11. Reinforcement Learning 12. Reduced Order Models (ROMs) 13. Interpolation for Parametric ROMs 14. Physics-Informed Machine Learning. Subject Areas: Signal processing [UYS], Machine learning [UYQM], Mathematical theory of computation [UYA], Automatic control engineering [TJFM], Mathematical physics [PHU], Mathematical modelling [PBWH], Optimization [PBU], Probability & statistics [PBT]
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