TripNet: Learning Large-Scale High-Fidelity 3D Car Aerodynamics with Triplane Networks

The method

Implicit neural fields meet automotive CFD

TripNet brings triplane representations — proven in neural rendering — to large-scale aerodynamic learning. Car geometry is encoded into three axis-aligned feature planes; any point in space can then be queried for aerodynamic quantities by interpolating plane features. The result is a continuous, resolution-independent surrogate for high-fidelity 3D car aerodynamics.

TripNet architecture: triplane encoding of car geometry with coordinate-based decoding of aerodynamic fields.

Contributions

What the paper delivers

Resolution-independent queries

Fields are decoded per-coordinate, so predictions aren't tied to a fixed mesh or point-cloud resolution.

Unified surface & volume modeling

One representation serves drag coefficients, surface pressure fields, and volumetric flow — trained on DrivAerNet++ at full scale.

Efficiency at scale

Triplanes hold 3D structure at 2D memory cost, making high-fidelity learning tractable on the largest public car-aerodynamics dataset.

Reference

Citation

@article{chen2025tripnet,
  title   = {TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics
             with Triplane Networks},
  author  = {Chen, Qian and Elrefaie, Mohamed and Dai, Angela and Ahmed, Faez},
  journal = {arXiv preprint arXiv:2503.17400},
  year    = {2025}
}

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