TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks

• URL: http://arxiv.org/abs/2503.17400v2
• Date: Fri, 23 May 2025 14:28:05 GMT
• Title: TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
• Authors: Qian Chen, Mohamed Elrefaie, Angela Dai, Faez Ahmed,
• Abstract summary: TripNet is a triplane-based neural framework that implicitly encodes 3D geometry into a compact, continuous feature map with fixed dimension.<n>TripNet achieves state-of-the-art performance on the DrivAerNet and DrivAerNet++ datasets, accurately predicting drag coefficients, surface pressure, and full 3D flow fields.
• Score: 27.577307360710545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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