Scientific Machine Learning

Catalog description

Credits 3. 3 Lecture Hours. Introduction to the algorithmic and computational foundations of regularizing scientific laws in machine learning algorithms; ODE and PDE discretization; automatic differentiation; physics-informed neural networks; physics-informed Gaussian processes; operator learning; inverse modeling; uncertainty quantification. Prerequisite: Graduate classification or approval of instructor.