Stochastic methods for solving high-dimensional partial differential equations
Numerical Analysis
2022-03-25 v1
Abstract
We propose algorithms for solving high-dimensional Partial Differential Equations (PDEs) that combine a probabilistic interpretation of PDEs, through Feynman-Kac representation, with sparse interpolation. Monte-Carlo methods and time-integration schemes are used to estimate pointwise evaluations of the solution of a PDE. We use a sequential control variates algorithm, where control variates are constructed based on successive approximations of the solution of the PDE. Two different algorithms are proposed, combining in different ways the sequential control variates algorithm and adaptive sparse interpolation. Numerical examples will illustrate the behavior of these algorithms.
Cite
@article{arxiv.1905.05423,
title = {Stochastic methods for solving high-dimensional partial differential equations},
author = {Marie Billaud-Friess and Arthur Macherey and Anthony Nouy and Clémentine Prieur},
journal= {arXiv preprint arXiv:1905.05423},
year = {2022}
}