Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies

TL;DR

This paper introduces Continuous Level Monte Carlo (CLMC), a novel extension of MLMC that adapts model hierarchy per sample using a continuous level parameter, improving efficiency in PDE applications.

Contribution

The paper develops CLMC, enabling sample-adaptive model hierarchies with unbiasedness and comparable complexity to MLMC, supported by theoretical proofs and numerical experiments.

Findings

CLMC provides unbiased estimates under certain conditions.

Numerical results show CLMC outperforms uniform refinement strategies.

Theoretical complexity matches that of traditional MLMC.

Abstract

In this paper, we present a generalisation of the Multilevel Monte Carlo (MLMC) method to a setting where the level parameter is a continuous variable. This Continuous Level Monte Carlo (CLMC) estimator provides a natural framework in PDE applications to adapt the model hierarchy to each sample. In addition, it can be made unbiased with respect to the expected value of the true quantity of interest provided the quantity of interest converges sufficiently fast. The practical implementation of the CLMC estimator is based on interpolating actual evaluations of the quantity of interest at a finite number of resolutions. As our new level parameter, we use the logarithm of a goal-oriented finite element error estimator for the accuracy of the quantity of interest. We prove the unbiasedness, as well as a complexity theorem that shows the same rate of complexity for CLMC as for MLMC. Finally, we provide some numerical evidence to support our theoretical results, by successfully testing CLMC on a standard PDE test problem. The numerical experiments demonstrate clear gains for sample-wise adaptive refinement strategies over uniform refinements.