Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations

TL;DR

This paper extends the full history recursive multilevel Picard (MLP) approximation method to systems of high-dimensional semilinear PDEs, demonstrating its efficiency and accuracy through extensive numerical simulations up to 1000 dimensions.

Contribution

The paper introduces an extension of the MLP approximation method to systems of semilinear PDEs and provides numerical evidence of its effectiveness in high dimensions.

Findings

MLP method accurately approximates high-dimensional PDE solutions

MLP outperforms certain deep learning methods in speed and accuracy

Effective for PDEs up to 1000 dimensions

Abstract

One of the most challenging issues in applied mathematics is to develop and analyze algorithms which are able to approximately compute solutions of high-dimensional nonlinear partial differential equations (PDEs). In particular, it is very hard to develop approximation algorithms which do not suffer under the curse of dimensionality in the sense that the number of computational operations needed by the algorithm to compute an approximation of accuracy $\epsilon > 0$ grows at most polynomially in both the reciprocal $1/\epsilon$ of the required accuracy and the dimension $d \in \mathbb{N}$ of the PDE. Recently, a new approximation method, the so-called full history recursive multilevel Picard (MLP) approximation method, has been introduced and, until today, this approximation scheme is the only approximation method in the scientific literature which has been proven to overcome the curse of dimensionality in the numerical approximation of semilinear PDEs with general time horizons. It is a key contribution of this article to extend the MLP approximation method to systems of semilinear PDEs and to numerically test it on several example PDEs. More specifically, we apply the proposed MLP approximation method in the case of Allen-Cahn PDEs, Sine-Gordon-type PDEs, systems of coupled semilinear heat PDEs, and semilinear Black-Scholes PDEs in up to 1000 dimensions. The presented numerical simulation results suggest in the case of each of these example PDEs that the proposed MLP approximation method produces very accurate results in short runtimes and, in particular, the presented numerical simulation results indicate that the proposed MLP approximation scheme significantly outperforms certain deep learning based approximation methods for high-dimensional semilinear PDEs.