Extensions to Multifidelity Monte Carlo Methods for Simulations of Chaotic Systems

TL;DR

This paper introduces a cost-effective correlation estimation method for multifidelity Monte Carlo simulations of chaotic systems, reducing preprocessing costs by avoiding high-fidelity model sampling through discretization error estimates.

Contribution

It develops a new correlation estimation procedure that leverages discretization errors, extending to chaotic systems by accounting for statistical and sampling errors.

Findings

Significantly reduces preprocessing costs in multifidelity Monte Carlo methods.

Effectively applied to a Kuramoto-Sivashinsky equation model.

Demonstrates accurate correlation estimates without high-fidelity sampling.

Abstract

Multifidelity Monte Carlo methods often rely on a preprocessing phase consisting of standard Monte Carlo sampling to estimate correlation coefficients between models of different fidelity to determine the weights and number of samples for each level. For computationally intensive models, as are often encountered in simulations of chaotic systems, this up-front cost can be prohibitive. In this work, a correlation estimation procedure is developed for the case in which the highest and next highest fidelity models are generated via discretizing the same mathematical model using different resolution. The procedure uses discretization error estimates to estimate the required correlation coefficient without the need to sample the highest fidelity model, which can dramatically decrease the cost of the preprocessing phase. The method is extended to chaotic problems by using discretization error estimates that account for the statistical nature of common quantities of interest and the accompanying finite sampling errors that pollute estimates of such quantities of interest. The methodology is then demonstrated on a model problem based on the Kuramoto-Sivashinsky equation.