Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
- Brunton, Steven L.
- Kutz, J. Nathan
- Jul 28, 2022
This textbook provides a comprehensive overview of data-driven methods, machine learning, and applied optimization in the context of engineering mathematics and mathematical physics. The second edition includes new chapters on reinforcement learning and physics-informed machine learning, making it a valuable resource for advanced undergraduate and beginning graduate students in the physical sciences. The book features online supplementary material, including lecture videos, homework, data, and code in MATLAB®, Python, Julia, and R. This book is a must-have for anyone interested in data-driven modeling and simulations.