Geometric deep learning for data-driven solid mechanics

This course focuses on a geometric learning approach to derive, test, and validate a wide range of artificial intelligence enabled models for engineering (meta-materials, composites, alloys) and natural materials (soil, rock, clay). Students will learn how to incorporate a wide range of data stored in graphs, manifold and point sets to train neural networks to design optimal experiments, embed high-dimensional data, enforce mechanics and physical principles, denoise data with geometry, and enable model-free simulations and discover causality of mechanisms that leads to the failures of materials. Prerequisite: Linear algebra and CEE291