Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies
For practitioners of PDE-based uncertainty quantification, CLMC offers a principled way to adapt model fidelity per sample, potentially reducing computational cost.
This paper generalizes Multilevel Monte Carlo (MLMC) to a continuous level parameter (CLMC), enabling sample-adaptive model hierarchies. The method is unbiased and achieves the same complexity rate as MLMC, with numerical experiments showing gains for adaptive refinement over uniform refinement.
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.