W24-12 Machine Learning for Solid Mechanics

Instructors: WaiChing Sun, Columbia University; JS Chen, University of California, San Diego; Nikolaos Vlassis, Rutgers University; Qizhi He, University of Minnesoata


This course will be offered to graduate students and researchers to introduce the practical data analytics, dimension reduction, and machine learning techniques for a variety of science and engineering applications in materials, structures, and systems.This course is designed for the audience with a background in mechanics and/or applied physics. The course will overview four major categories of machine learning techniques (dimensional reduction of manifold data, generative artificial intelligence (denoising diffusion probabilistic models) and symbolic regression ad knowledge graph for interpretable scientific machine learning. Case studies will demonstrate how these learning techniques have enhanced research and technology advancements. These application problems will include a data-driven model-free paradigm for complex material systems, reduced-order modeling of fracture and thermal fatigue analysis, geometric learning for polycrystal and energetic materials. Lecture materials and lab handouts will be provided before the short course.