Speaker
Description
We present a novel variant of the multi-level Monte Carlo method that effectively
utilizes a reserved computational budget on a high-performance computing system to minimize the
mean squared error. Our approach combines concepts of the continuation multi-level Monte Carlo
method with dynamic programming techniques following Bellman’s optimality principle, and a new
parallelization strategy based on a single distributed data structure. Additionally, we establish
a theoretical bound on the error reduction on a parallel computing cluster and provide empirical
evidence that the proposed method adheres to this bound. We implement, test, and benchmark
the approach on computationally demanding problems, focusing on its application to acoustic wave
propagation in high-dimensional random media.
