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Importance Sampling for Rare-event Simulation of Random Elliptic Equations
Date: 2018/5/24             Browse: 117

Speaker:     Dr. Xiang Zhou. CityU HK

Time:          10:15—11:15, May 24 

Location:    Room 1A-200, SIST Building

Host:          Prof. Qifeng Liao


We present the efficient Monte Carlo estimation of the small probability related to the solution of elliptic PDE subject to random effects. These problems are motived by the material failure. The first is on the random force and the L2 norm of the solution. The second is on the random coefficient and the L_inf norm of the solution. We discuss the underlying eigenvalue problem related to the minimizer of the rate function in the large deviation principle and the weak efficiency of the exponential tilt change of measure in the importance sampling. The work is supported by the Hong Kong GRF.


Dr. Xiang Zhou received his BSc from Peking University and PhD from Princeton University. Before joining City University in 2012, he worked as a research associate at Princeton University and Brown University. His major research area is the study of rare event. His research interests include the development and analysis of algorithms for transitions in nonlinear stochastic dynamical systems, the efficient Monte Carlo simulation of rare events, the numerical methods for saddle point and the exploration of high dimensional non-convex energy landscapes in physical models and machine learning models. His research results have turned into peer-reviewed papers in SIAM journals, Journal of Computational Physics, Journal of Chemical Physics, Nonlinearity and Annals of Applied Probability, etc.

SIST-Seminar 18031