AB-UPT模型仅用单块GPU一天即可训练，秒级预测汽车飞机气动性能。
AB-UPT for Automotive and Aerospace Applications
- 基于分枝锚定的物理变换器架构，以简化几何表示实现高效建模。
- 在汽车与飞机数据集上均超越现有最先进方法，积分气动力预测接近完美。
- 适合需要快速仿真与工业级部署的自动驾驶与航空航天研发团队。
最近提出的锚定分枝通用物理变换器（AB-UPT）展现出强大能力，可在远低于传统数值求解器所需计算量的情况下复现汽车计算流体动力学模拟。本技术报告中，我们向已有的实证使用案例库新增两个数据集，结合高质量数据生成与最先进的神经代理模型。两个数据集均由Luminary Cloud平台生成，涵盖汽车（SHIFT-SUV）和飞机（SHIFT-Wing）。首先详细说明数据生成过程，随后展示AB-UPT在两个数据集上对先前最先进基于Transformer的基线方法的优异表现，并对最佳AB-UPT模型进行广泛的定性与定量评估。结果显示，AB-UPT整体性能突出：仅需数秒即可从简单的各向同性网格几何表示中获得近乎完美的积分气动力预测，且可在单块GPU上一天内完成训练，为工业级应用铺平道路。
原文摘要 · Abstract (English)
The recently proposed Anchored-Branched Universal Physics Transformers (AB-UPT) shows strong capabilities to replicate automotive computational fluid dynamics simulations requiring orders of magnitudes less compute than traditional numerical solvers. In this technical report, we add two new datasets to the body of empirically evaluated use-cases of AB-UPT, combining high-quality data generation with state-of-the-art neural surrogates. Both datasets were generated with the Luminary Cloud platform containing automotives (SHIFT-SUV) and aircrafts (SHIFT-Wing). We start by detailing the data generation. Next, we show favorable performances of AB-UPT against previous state-of-the-art transformer-based baselines on both datasets, followed by extensive qualitative and quantitative evaluations of our best AB-UPT model. AB-UPT shows strong performances across the board. Notably, it obtains near perfect prediction of integrated aerodynamic forces within seconds from a simple isotopically tesselate geometry representation and is trainable within a day on a single GPU, paving the way for industry-scale applications.
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