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English(EN) RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics

RETO Transformer算子通过RoPE增强汽车空气动力学预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的旋转增强Transformer算子RETO，旨在改进汽车空气动力学预测。该新模型采用双阶段空间感知机制，利用正弦-余弦编码和旋转位置编码（RoPE）来更好地捕捉复杂的空间相关性。RETO在ShapeNet和DrivAerML基准测试中均显著优于现有基线模型，在表面压力和速度预测方面取得了显著的准确性提升。 AI

影响 引入了一种新颖的空气动力学预测神经网络算子，有望改进汽车设计工作流程。

排序理由 这是一篇介绍新模型（RETO）并展示基准测试结果的研究论文。

在 arXiv cs.LG 阅读 →

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RETO Transformer算子通过RoPE增强汽车空气动力学预测

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bojun Zhang, Huiyu Yang, Yunpeng Wang, Yuntian Chen, Yuanwei Bin, Rikui Zhang, Jianchun Wang ·

    RETO：一种用于汽车空气动力学高保真预测的旋转增强Transformer算子

    arXiv:2605.00062v1 Announce Type: cross Abstract: Rapid aerodynamic evaluation is crucial for modern vehicle design, yet existing neural operators struggle to capture intricate spatial correlations. We propose the rotary-enhanced transformer operator (RETO), a novel neural solver…

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