SE 232Machine Learning in Computational Mechanics
Provides background and tools to apply machine learning to solve problems in computational mechanics and engineering. An overview of the basic principles of machine learning will be provided, including supervised and unsupervised learning, regression, classification, and generative algorithms versus discriminative algorithms. Focus will be given to deep neural networks, convolutional neural networks, recurrent neural networks, physics-informed machine learning and implementation in Python. Recommended preparation: knowledge of computer programming, probability theory, linear algebra, and solid mechanics.
Prerequisites
From the catalog: “graduate standing or consent of instructor.”