New 2nd Edition of our book: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

DOWNLOAD 2ND ED PDF: https://faculty.washington.edu/sbrunt...
1ST ED PDF: https://databookuw.com/databook.pdf

AMAZON: https://www.amazon.com/Data-Driven-Sc...

CAMBRIDGE: https://www.cambridge.org/highereduca...

CODES: https://github.com/dynamicslab/

NEW IN THIS EDITION:
* New Chapters:
* Reinforcement learning
* Physics-informed machine learning
* Code in Python and Matlab
* Homework for every chapter ranging from introductory topics to advanced projects
* Videos for every section
* 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

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.