Stochastic methods for solving high-dimensional partial differential equations. (English) Zbl 07240092
Tuffin, Bruno (ed.) et al., Monte Carlo and quasi-Monte Carlo methods. MCQMC 2018. Proceedings of the 13th international conference on Monte Carlo and quasi-Monte Carlo methods in scientific computing, Rennes, France, July 1–6, 2018. Cham: Springer. Springer Proc. Math. Stat. 324, 125-141 (2020).
Summary: 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.
For the entire collection see [Zbl 1440.65006].
For the entire collection see [Zbl 1440.65006].
MSC:
| 65C05 | Monte Carlo methods |
References:
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