Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication

TL;DR

This paper introduces a machine learning-based decision model that dynamically chooses between particle and continuum simulations during membrane fabrication, significantly improving computational efficiency and scalability in multiscale modeling.

Contribution

The paper presents a novel MLP-based decision model for adaptive coupling of simulation methods, enhancing efficiency in multiscale membrane fabrication simulations.

Findings

The decision model accurately predicts when to switch simulation methods.

Adaptive coupling reduces computational costs by focusing high-fidelity simulations where needed.

The approach enables scalable and efficient multiscale membrane process simulations.

Abstract

All simulation approaches eventually face limits in computational scalability when applied to large spatiotemporal domains. This challenge becomes especially apparent in molecular-level particle simulations, where high spatial and temporal resolution leads to rapidly increasing computational demands. To overcome these limitations, hybrid methods that combine simulations with different levels of resolution offer a promising solution. In this context, we present a machine learning-based decision model that dynamically selects between simulation methods at runtime. The model is built around a Multilayer perceptron (MLP) that predicts the expected discrepancy between particle and continuum simulation results, enabling the localized use of high-fidelity particle simulations only where they are expected to add value. This concurrent approach is applied to the simulation of membrane fabrication processes, where a particle simulation is coupled with a continuum model. This article describes the architecture of the decision model and its integration into the simulation workflow, enabling efficient, scalable, and adaptive multiscale simulations.