16.940 Numerical Methods for Stochastic Modeling and Inference
Spring 2014
Instructors: Youssef M Marzouk, Florian Markus Augustin, Tiangang Cui, Antti Solonen
Lecture: TR11-12.30 (37-212)
Information:
Advanced introduction to numerical methods for treating uncertainty in computational simulation. Draws examples from a range of science and engineering applications, emphasizing systems governed by ordinary and partial differential equations. Uncertainty propagation and assessment: Monte Carlo methods, variance reduction, sensitivity analysis, adjoint methods, polynomial chaos and Karhunen-Loève expansions, and stochastic Galerkin and collocation methods. Interaction of models with observational data, from the perspective of statistical inference: Bayesian parameter estimation, statistical regularization, Markov chain Monte Carlo, sequential data assimilation and filtering, model selection, and model error. Computer assignments require programming.
Announcements
Final project presentations
Final project presentations (10 minutes each) are on Thursday May 15. Please sign up for a slot at https://signup.mit.edu:444/signup.php?id=16940finalAnnounced on 10 May 2014 4:10 p.m. by Youssef M Marzouk
