Physics-Informed Machine Learning, Section 1 - Introduction, Part 1

Kick off this series of nine lectures with an overview of Physics-Informed Machine Learning (PIML), outlining the course structure and four case studies. This session introduces the motivation for PIML and explains how integrating physics-based constraints with machine learning (ML) can address complex multi-physics challenges in engineering. Instructor: Dr. Navid Zobeiry, Associate Professor of Materials Science and Engineering, and Adjunct Professor of Aeronautics and Astronautics, University of Washington Accompanying Codes and Slides: https://composites.uw.edu/AI/ This video was produced at the University of Washington, and we acknowledge funding support from The Boeing Company. Keywords: Physics-Informed Machine Learning, Machine Learning for Engineering, AI in Science, Multi-Physics Modeling, Data-Driven Science, Deep Learning for Physics, Neural Networks in Engineering