This video provides a brief recap of this introductory series on Physics Informed Machine Learning. We revisit the five stages of machine learning, and how physics may be incorporated into these stages. We also discuss architectures, symmetries, the digital twin, applications in engineering, and the importance of dynamical systems and controls benchmarks.

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

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00:00 Intro
00:24 Future Modules
06:06 Curriculum Framework
07:02 The Dual Problems of PIML
08:45 Data-Driven Science and Engineering
09:12 Sneak Peak of the Modules
09:35 Sneak Peak: Parsimonious Models
11:13 Sneak Peak: PINNs
12:47 Sneak Peak: Operator Methods
14:10 Sneak Peak: Symmetries
15:42 Sneak Peak: Digital Twins
17:35 Sneak Peak: Case Studies & Benchmarks
18:24 Outro