HGPTrans: Hierarchical Graph-Pooling Transolver for Automotive Aerodynamic Drag Coefficient Prediction

arXiv:2609.31765 · cs.CV, physics.flu-dyn · Submitted 2026-09-24 · Read on arXiv

cs.CV, physics.flu-dyn

Submitted: 2026-09-24

Updated: 2026-10-07

Code: https://github.com/BoLiu-USTC/HGPTrans

Terminology

• Accuracy — 18× in this paper · explained in 1 episode
• Benchmark — 17× in this paper · explained in 2 episodes
• Aggregation — 15× in this paper · explained in 1 episode
• Ablation — 14× in this paper · explained in 1 episode
• Fidelity — 11× in this paper · explained in 2 episodes
• Transfer Learning — 11× in this paper · explained in 10 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Ablation Studies — 5× in this paper · explained in 1 episode
• Redundancy — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Aerodynamic Optimization — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Neural Surrogates — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Distribution Shift — 1× in this paper · explained in 5 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes

Sources

• Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
• DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
• Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
• DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations
• Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries
• Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction
• CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics

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