MTH 602: Scientific Machine Learning - fall

Prerequisites: EAS 520/DSC 520, EAS 501, and EAS 502; or DSC graduate student; or permission of the instructor

Scientific machine learning algorithms for computational science and engineering. Topics may include physics-informed neural networks, neural dynamical systems, AI-based surrogate models, signal detection with convolutional neural networks, learning nonlinear continuous operators, neural turbulence models, optimization algorithms, simulation-based Bayesian inference, and more. Python will be the primary language. Emphasis on real-world applications, covering high-performance computing with multi-core and GPU acceleration.