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…
Includes code snippets in both Python and MATLAB®
New chapters on reinforcement learning and physics-informed machine learning
New sections throughout, with topics including condition number and error bounds for the SVD; autoencoders, recurrent neural networks, and generative adversarial networks; and neural networks for reduced-order models
Pages of new exercises for every chapter ranging from introductory topics to advanced projects along with simple explanations
Beautiful colour illustrations and diagrams
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