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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.02974 |
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| _version_ | 1866910259173392384 |
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| author | Casenave, Fabien Roynard, Xavier Staber, Brian Devaux-Rivière, Alexandre Piat, William Bucci, Michele Alessandro Akkari, Nissrine Kabalan, Abbas Nguyen, Xuan Minh Vuong Saverio, Luca Perez, Raphaël Carpintero Kalaydjian, Anthony Fouché, Samy Gonon, Thierry Najjar, Ghassan Daniel, Thomas Menier, Emmanuel Nastorg, Matthieu Catalani, Giovanni Rey, Christian |
| author_facet | Casenave, Fabien Roynard, Xavier Staber, Brian Devaux-Rivière, Alexandre Piat, William Bucci, Michele Alessandro Akkari, Nissrine Kabalan, Abbas Nguyen, Xuan Minh Vuong Saverio, Luca Perez, Raphaël Carpintero Kalaydjian, Anthony Fouché, Samy Gonon, Thierry Najjar, Ghassan Daniel, Thomas Menier, Emmanuel Nastorg, Matthieu Catalani, Giovanni Rey, Christian |
| contents | Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-scale, diverse, and standardized datasets for physics-based simulations. Existing benchmarks often focus on narrow domains or rely on simplified data models, and fail to capture the heterogeneity arising from variable geometries, meshes, and topologies, which is critical for assessing generalization in realistic settings. We introduce PLAID (Physics-Learning AI Data model), a unified and extensible data layer for heterogeneous physics simulations. It preserves the full complexity of simulation data while enabling efficient and scalable machine learning workflows, together with a library for dataset construction and manipulation~(\href{https://github.com/PLAID-lib/plaid}{github.com/PLAID-lib/plaid}). We release six datasets covering structural mechanics and computational fluid dynamics, designed to reflect realistic industrial scenarios and provide standardized benchmarks. The framework includes reproducible evaluation protocols and is integrated with Hugging Face to enable open, community-driven benchmarking with active user participation (\href{https://huggingface.co/PLAIDcompetitions}{huggingface.co/PLAIDcompetitions}). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations Casenave, Fabien Roynard, Xavier Staber, Brian Devaux-Rivière, Alexandre Piat, William Bucci, Michele Alessandro Akkari, Nissrine Kabalan, Abbas Nguyen, Xuan Minh Vuong Saverio, Luca Perez, Raphaël Carpintero Kalaydjian, Anthony Fouché, Samy Gonon, Thierry Najjar, Ghassan Daniel, Thomas Menier, Emmanuel Nastorg, Matthieu Catalani, Giovanni Rey, Christian Machine Learning Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-scale, diverse, and standardized datasets for physics-based simulations. Existing benchmarks often focus on narrow domains or rely on simplified data models, and fail to capture the heterogeneity arising from variable geometries, meshes, and topologies, which is critical for assessing generalization in realistic settings. We introduce PLAID (Physics-Learning AI Data model), a unified and extensible data layer for heterogeneous physics simulations. It preserves the full complexity of simulation data while enabling efficient and scalable machine learning workflows, together with a library for dataset construction and manipulation~(\href{https://github.com/PLAID-lib/plaid}{github.com/PLAID-lib/plaid}). We release six datasets covering structural mechanics and computational fluid dynamics, designed to reflect realistic industrial scenarios and provide standardized benchmarks. The framework includes reproducible evaluation protocols and is integrated with Hugging Face to enable open, community-driven benchmarking with active user participation (\href{https://huggingface.co/PLAIDcompetitions}{huggingface.co/PLAIDcompetitions}). |
| title | PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.02974 |