Multi-Level Monte Carlo sampling with Parallel-in-Time Integration for Uncertainty Quantification in Electric Machine Simulation

TL;DR

This paper introduces a novel uncertainty quantification approach combining Multi-Level Monte Carlo sampling with Parallel-in-Time integration, significantly reducing time-to-solution in electric machine simulations at the cost of increased computational effort.

Contribution

It proposes a new method that integrates Multi-Level Monte Carlo with Parallel-in-Time techniques to accelerate uncertainty quantification in high-dimensional problems.

Findings

Achieved 12-45% speedup over standard Multi-Level Monte Carlo.

Increased total computational effort by 15-18%.

Validated the approach with two numerical examples.

Abstract

While generally considered computationally expensive, Uncertainty Quantification using Monte Carlo sampling remains beneficial for applications with uncertainties of high dimension. As an extension of the naive Monte Carlo method, the Multi-Level Monte Carlo method reduces the overall computational effort, but is unable to reduce the time to solution in a sufficiently parallel computing environment. In this work, we propose a Uncertainty Quantification method combining Multi-Level Monte Carlo sampling and Parallel-in-Time integration for select samples, exploiting remaining parallel computing capacity to accelerate the computation. While effective at reducing the time-to-solution, Parallel-in-Time integration methods greatly increase the total computational effort. We investigate the tradeoff between time-to-solution and total computational effort of the combined method, starting from theoretical considerations and comparing our findings to two numerical examples. There, a speedup of 12 - 45% compared to Multi-Level Monte Carlo sampling is observed, with an increase of 15 - 18% in computational effort.