Courses taught
GEN_ENG 205-4
Engineering Analysis IV
Exact, numerical, and qualitative methods for solving ordinary differential equations, with an emphasis on applications and mathematical modeling.
ME 697
Advanced Scientific Machine Learning
An advanced course exploring state-of-the-art methods in scientific machine learning, including neural operators, infinite-dimensional Bayesian inverse problems, sparse identification of nonlinear dynamics (SINDy), and calibration of stochastic differential equations. Students learn functional programming in JAX and use differentiable and probabilistic programming to implement these methods.
Online textbook by Ilias BilionisME 539
Introduction to Scientific Machine Learning
An introduction to data science and machine learning requiring no prior experience in either field. Topics include probability, Monte Carlo uncertainty propagation, supervised and unsupervised learning, state space models, physics-informed deep learning, and Bayesian inference. A focus on first principles prepares students to build and fit their own models.
Online textbook by Ilias Bilionis