Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering

This video describes how to incorporate physics into the machine learning process. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process. Physics informed machine learning is critical for many engineering applications, since many engineering systems are governed by physics and involve safety critical components. It also makes it possible to learn more from sparse and noisy data sets. %%% CHAPTERS %%% 00:00 Intro 03:53 What is Physics Informed Machine Learning? 06:41 Case Study: Encoding Pendulum Movement 09:19 The Five Stages of Machine Learning 16:09 A Principled Approach to Machine Learning 20:00 Physics Informed Problem Modeling 21:48 Physics Informed Data Curation 25:34 Physics Informed Architecture Design 28:59 Physics Informed Loss Functions 30:55 Physics Informed Optimization Algorithms 34:56 What This Course Will Cover 46:48 Outro