Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915569466343424 |
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| author | Busch, Matthias Häfner, Gregor Xie, Jiayu Tacke, Marius Müller, Marcus Cyron, Christian J. Aydin, Roland C. |
| author_facet | Busch, Matthias Häfner, Gregor Xie, Jiayu Tacke, Marius Müller, Marcus Cyron, Christian J. Aydin, Roland C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication Busch, Matthias Häfner, Gregor Xie, Jiayu Tacke, Marius Müller, Marcus Cyron, Christian J. Aydin, Roland C. Computational Physics Materials Science Soft Condensed Matter 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. |
| title | Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication |
| topic | Computational Physics Materials Science Soft Condensed Matter |
| url | https://arxiv.org/abs/2510.19051 |