FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916034029551616 |
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| author | Wang, Haixin Li, Ruoyan Xu, Fred Sun, Fang Han, Kaiqiao Huang, Zijie Chang, Ching Luo, Xiao Wang, Wei Sun, Yizhou |
| author_facet | Wang, Haixin Li, Ruoyan Xu, Fred Sun, Fang Han, Kaiqiao Huang, Zijie Chang, Ching Luo, Xiao Wang, Wei Sun, Yizhou |
| contents | Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets and standardized evaluation protocols. Although architectural innovations are abundant, fair assessment is further impeded by the lack of clear disentanglement between spatial, temporal and loss modules. In this paper, we introduce FD-Bench, the first fair, modular, comprehensive and reproducible benchmark for data-driven fluid simulation. FD-Bench systematically evaluates 85 baseline models across 10 representative flow scenarios under a unified experimental setup. It provides four key contributions: (1) a modular design enabling fair comparisons across spatial, temporal, and loss function modules; (2) the first systematic framework for direct comparison with traditional numerical solvers; (3) fine-grained generalization analysis across resolutions, initial conditions, and temporal windows; and (4) a user-friendly, extensible codebase to support future research. Through rigorous empirical studies, FD-Bench establishes the most comprehensive leaderboard to date, resolving long-standing issues in reproducibility and comparability, and laying a foundation for robust evaluation of future data-driven fluid models. The code is open-sourced at https://github.com/WillDreamer/FD-Bench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20349 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation Wang, Haixin Li, Ruoyan Xu, Fred Sun, Fang Han, Kaiqiao Huang, Zijie Chang, Ching Luo, Xiao Wang, Wei Sun, Yizhou Fluid Dynamics Machine Learning Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets and standardized evaluation protocols. Although architectural innovations are abundant, fair assessment is further impeded by the lack of clear disentanglement between spatial, temporal and loss modules. In this paper, we introduce FD-Bench, the first fair, modular, comprehensive and reproducible benchmark for data-driven fluid simulation. FD-Bench systematically evaluates 85 baseline models across 10 representative flow scenarios under a unified experimental setup. It provides four key contributions: (1) a modular design enabling fair comparisons across spatial, temporal, and loss function modules; (2) the first systematic framework for direct comparison with traditional numerical solvers; (3) fine-grained generalization analysis across resolutions, initial conditions, and temporal windows; and (4) a user-friendly, extensible codebase to support future research. Through rigorous empirical studies, FD-Bench establishes the most comprehensive leaderboard to date, resolving long-standing issues in reproducibility and comparability, and laying a foundation for robust evaluation of future data-driven fluid models. The code is open-sourced at https://github.com/WillDreamer/FD-Bench. |
| title | FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2505.20349 |