Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

This comprehensive textbook offers a broad overview of data-driven discovery, machine learning, applied optimization, and classical engineering mathematics for advanced undergraduate and beginning graduate students in the physical sciences.

With new chapters on reinforcement learning and physics-informed machine learning, this second edition provides relevant and accessible material for those interested in integrating dynamical systems modeling and control with modern methods in applied machine learning.

The text includes chapter exercises and online supplementary material, making it an excellent resource for anyone looking to gain a deeper understanding of data-driven discovery and its applications in various fields.