On the Selection of Random Field Evaluation Points in the p-MLQMC Method

TL;DR

This paper explores optimal strategies for selecting evaluation points of random fields in the p-MLQMC method to improve variance reduction and computational efficiency in uncertainty quantification for geotechnical engineering problems.

Contribution

It introduces and compares three approaches for selecting evaluation points in the p-MLQMC method, demonstrating significant speedups in slope stability simulations.

Findings

Local Nested Approach achieves up to five times faster computation.

The study benchmarks approaches on a slope stability problem.

Variance reduction depends on the evaluation point selection method.

Abstract

Engineering problems are often characterized by significant uncertainty in their material parameters. A typical example coming from geotechnical engineering is the slope stability problem where the soil's cohesion is modeled as a random field. An efficient manner to account for this uncertainty is the novel sampling method called p-refined Multilevel Quasi-Monte Carlo (p-MLQMC). The p-MLQMC method uses a hierarchy of p-refined Finite Element meshes combined with a deterministic Quasi-Monte Carlo sampling rule. This combination yields a significant computational cost reduction with respect to classic Multilevel Monte Carlo. However, in previous work, not enough consideration was given how to incorporate the uncertainty, modeled as a random field, in the Finite Element model with the p-MLQMC method. In the present work we investigate how this can be adequately achieved by means of the integration point method. We therefore investigate how the evaluation points of the random field are to be selected in order to obtain a variance reduction over the levels. We consider three different approaches. These approaches will be benchmarked on a slope stability problem in terms of computational runtime. We find that for a given tolerance the Local Nested Approach yields a speedup up to a factor five with respect to the Non-Nested approach.