TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913854286462976 |
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| author | Chen, Qian Elrefaie, Mohamed Dai, Angela Ahmed, Faez |
| author_facet | Chen, Qian Elrefaie, Mohamed Dai, Angela Ahmed, Faez |
| contents | Surrogate modeling has emerged as a powerful tool to accelerate Computational Fluid Dynamics (CFD) simulations. Existing 3D geometric learning models based on point clouds, voxels, meshes, or graphs depend on explicit geometric representations that are memory-intensive and resolution-limited. For large-scale simulations with millions of nodes and cells, existing models require aggressive downsampling due to their dependence on mesh resolution, resulting in degraded accuracy. We present TripNet, a triplane-based neural framework that implicitly encodes 3D geometry into a compact, continuous feature map with fixed dimension. Unlike mesh-dependent approaches, TripNet scales to high-resolution simulations without increasing memory cost, and enables CFD predictions at arbitrary spatial locations in a query-based fashion, independent of mesh connectivity or predefined nodes. TripNet achieves state-of-the-art performance on the DrivAerNet and DrivAerNet++ datasets, accurately predicting drag coefficients, surface pressure, and full 3D flow fields. With a unified triplane backbone supporting multiple simulation tasks, TripNet offers a scalable, accurate, and efficient alternative to traditional CFD solvers and existing surrogate models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks Chen, Qian Elrefaie, Mohamed Dai, Angela Ahmed, Faez Fluid Dynamics Machine Learning Surrogate modeling has emerged as a powerful tool to accelerate Computational Fluid Dynamics (CFD) simulations. Existing 3D geometric learning models based on point clouds, voxels, meshes, or graphs depend on explicit geometric representations that are memory-intensive and resolution-limited. For large-scale simulations with millions of nodes and cells, existing models require aggressive downsampling due to their dependence on mesh resolution, resulting in degraded accuracy. We present TripNet, a triplane-based neural framework that implicitly encodes 3D geometry into a compact, continuous feature map with fixed dimension. Unlike mesh-dependent approaches, TripNet scales to high-resolution simulations without increasing memory cost, and enables CFD predictions at arbitrary spatial locations in a query-based fashion, independent of mesh connectivity or predefined nodes. TripNet achieves state-of-the-art performance on the DrivAerNet and DrivAerNet++ datasets, accurately predicting drag coefficients, surface pressure, and full 3D flow fields. With a unified triplane backbone supporting multiple simulation tasks, TripNet offers a scalable, accurate, and efficient alternative to traditional CFD solvers and existing surrogate models. |
| title | TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2503.17400 |