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Subject offerings of new and developing areas of knowledge in mechanical engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Theory and practice of Scientific Machine Learning (SciML), which leverages machine learning tools for scientific computing. Topics include learning-based methods for differential equations, neural ODEs and PDEs, physics-informed networks and model discovery, interpretable and explainable learning, differentiable and probabilistic programming for scientific computing, and uncertainty quantification via learning. Efficient parallel implementation of algorithms on scalable computing architectures will be emphasized. Requires familiarity with introductory numerical methods (e.g., TAM 470 or CS 357) and the basics of machine learning and neural networks (e.g., CS 446).