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 pages
25.9 x 18.3 x 3.1 cm, 1.4 kg

'This is a must read for those who are interested in understanding what machine learning can do for dynamical systems! Steve and Nathan have done an excellent job in bringing everyone up to speed to the modern application of machine learning on these complex dynamical systems.' Shirley Ho, Flatiron Institute/New York University

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]