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.
@article{arxiv.2510.19051,
title = {Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication},
author = {Matthias Busch and Gregor Häfner and Jiayu Xie and Marius Tacke and Marcus Müller and Christian J. Cyron and Roland C. Aydin},
journal= {arXiv preprint arXiv:2510.19051},
year = {2025}
}