An Uncertainty-aware, Mesh-free Numerical Method for Kolmogorov PDEs

TL;DR

This paper presents a novel mesh-free numerical approach for solving Kolmogorov PDEs that leverages Gaussian process regression for uncertainty quantification and efficient high-dimensional computation.

Contribution

It introduces an uncertainty-aware, mesh-free method combining GPR with Monte Carlo solutions, enhancing accuracy and robustness for high-dimensional PDEs.

Findings

High accuracy demonstrated on three PDEs

Effective uncertainty quantification via GPR

Robust performance compared to existing methods

Abstract

This study introduces an uncertainty-aware, mesh-free numerical method for solving Kolmogorov PDEs. In the proposed method, we use Gaussian process regression (GPR) to smoothly interpolate pointwise solutions that are obtained by Monte Carlo methods based on the Feynman-Kac formula. The proposed method has two main advantages: 1. uncertainty assessment, which is facilitated by the probabilistic nature of GPR, and 2. mesh-free computation, which allows efficient handling of high-dimensional PDEs. The quality of the solution is improved by adjusting the kernel function and incorporating noise information from the Monte Carlo samples into the GPR noise model. The performance of the method is rigorously analyzed based on a theoretical lower bound on the posterior variance, which serves as a measure of the error between the numerical and true solutions. Extensive tests on three representative PDEs demonstrate the high accuracy and robustness of the method compared to existing methods.