CS 598 SML

Subject offerings of new and developing areas of knowledge in computer science 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.

Scientific Machine Learning. Description: This course will cover the 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. Prerequisite: Familiarity with introductory numerical methods (e.g., CS 357, CS 450, or TAM 470), the basics of machine learning and neural networks, and some knowledge of numerical methods for PDEs (e.g. CS555 or similar) is expected. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister