Statistics for stochastic differential equations and approximations of resolvent

TL;DR

This paper introduces a second-order approximation algorithm for evaluating statistics of stochastic differential equations, improving accuracy and efficiency in computational physics applications.

Contribution

It presents a higher-order resolvent approximation and an algorithm with second-order convergence, enhancing existing methods for stochastic differential equations.

Findings

The second-order algorithm achieves second-order convergence.

Extrapolation methods improve accuracy with less computational effort.

Demonstrated effectiveness on Ornstein-Uhlenbeck and van der Pol systems.

Abstract

The numerical evaluation of statistics plays a crucial role in statistical physics and its applied fields. It is possible to evaluate the statistics for a stochastic differential equation with Gaussian white noise via the corresponding backward Kolmogorov equation. The important notice is that there is no need to obtain the solution of the backward Kolmogorov equation on the whole domain; it is enough to evaluate a value of the solution at a certain point that corresponds to the initial coordinate for the stochastic differential equation. For this aim, an algorithm based on combinatorics has recently been developed. In this paper, we discuss a higher-order approximation of resolvent, and an algorithm based on a second-order approximation is proposed. The proposed algorithm shows a second-order convergence. Furthermore, the convergence property of the naive algorithms naturally leads to extrapolation methods; they work well to calculate a more accurate value with fewer computational costs. The proposed method is demonstrated with the Ornstein-Uhlenbeck process and the noisy van der Pol system.