Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
<|reference_start|>Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim: Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems. However, CFD+ML algorithms require exchange of data, synchronization, and calculation on heterogeneous hardware, making their implementation for large-scale problems exceptionally challenging.   We provide an effective and scalable solution to developing CFD+ML algorithms using open source software OpenFOAM and SmartSim. SmartSim provides an Orchestrator that significantly simplifies the programming of CFD+ML algorithms and a Redis database that ensures highly scalable data exchange between ML and CFD clients. We show how to leverage SmartSim to effectively couple different segments of OpenFOAM with ML, including pre/post-processing applications, solvers, function objects, and mesh motion solvers. We additionally provide an OpenFOAM sub-module with examples that can be used as starting points for real-world applications in CFD+ML.<|reference_end|>
@article{maric2024combining,
    title={Combining Machine Learning with Computational Fluid Dynamics using
  OpenFOAM and SmartSim},
    author={Tomislav Maric and Mohammed Elwardi Fadeli and Alessandro Rigazzi and
  Andrew Shao and Andre Weiner},
    journal={Meccanica, 2024},
    year={2024},
    doi={10.1007/s11012-024-01797-z},
    archivePrefix={arXiv},
    eprint={2402.16196},
    primaryClass={cs.LG physics.flu-dyn}
}