用三平面隐式编码实现高精度汽车空气动力学模拟，突破传统模型分辨率与内存限制。
TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
- 采用三平面结构将3D几何隐式编码为固定维度的连续特征图，无需依赖网格
- 在DrivAerNet和DrivAerNet++数据集上准确预测阻力系数、表面压力及完整流场
- 支持任意位置查询，适合大规模高分辨率仿真，可替代传统CFD求解器
代理建模已成为加速计算流体动力学（CFD）模拟的强大工具。现有基于点云、体素、网格或图的3D几何学习模型依赖显式几何表示，内存占用高且分辨率受限。对于包含数百万节点和单元的大规模模拟，现有模型因依赖网格分辨率而需大幅下采样，导致精度下降。本文提出TripNet，一种基于三平面的神经框架，将3D几何隐式编码为固定维度的连续特征图。不同于依赖网格的方法，TripNet可在不增加内存成本的情况下扩展至高分辨率模拟，并以查询方式在任意空间位置进行CFD预测，独立于网格连接性或预定义节点。TripNet在DrivAerNet和DrivAerNet++数据集上达到当前最佳性能，能准确预测阻力系数、表面压力及完整的3D流场。通过统一的三平面主干网络支持多种仿真任务，TripNet为传统CFD求解器和现有代理模型提供了一种可扩展、高精度且高效的替代方案。
原文摘要 · Abstract (English)
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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