PLAID provides a CGNS-based data model, an open-source library, six new simulation datasets, and Hugging Face leaderboards for machine-learning surrogates.
Direct Prediction of Steady-State Flow Fields in Meshed Domain with Graph Networks
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abstract
We propose a model to directly predict the steady-state flow field for a given geometry setup. The setup is an Eulerian representation of the fluid flow as a meshed domain. We introduce a graph network architecture to process the mesh-space simulation as a graph. The benefit of our model is a strong understanding of the global physical system, while being able to explore the local structure. This is essential to perform direct prediction and is thus superior to other existing methods.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
PLAID provides a CGNS-based data model, an open-source library, six new simulation datasets, and Hugging Face leaderboards for machine-learning surrogates.