AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches Teaching Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design A course on modern numerical methods for random partial differential equations
