Data-Driven Modeling in Science and Engineering / Spring 2024
Course Description
How do we go from a high dimensional, noisy, nonlinear, complex, and multiscale universe to simple and predictive mathematical models? This course introduces modern machine learning techniques using a wide variety of examples in physical, social and biological sciences. Modern data-driven approaches that take advantage of recent advances in machine learning are introduced, including: sparse identification of differential equations, dynamics mode decomposition, and physics informed neural networks.
