English(EN) Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields

流体动力学中神经网络代理的无标签训练方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-23 04:00

研究人员开发了一种新颖的热流场预测神经网络代理训练方法，该方法基于最小化有限体积法（FVM）残差的无标签方法。该技术应用于注意力图神经网络，绕过了传统数值求解器生成的昂贵标记训练数据的需求。FVM-loss模型表现强劲，在稳态基准测试中实现了低误差率，并在瞬态情况下优于数据监督方法，同时消除了数据生成费用。 AI

影响这种无标签训练方法可以显著降低科学模拟神经网络代理的开发成本和复杂性。

排序理由该条目描述了一种在学术论文中提出的科学领域神经网络训练的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tianyu Li, Zhiwei Cao, Qingang Zhang, Ruihang Wang, Binyang Song, Yonggang Wen · 2026-07-23 04:00

用于耦合热流体场的无标签有限体积残差注意力图神经网络训练

arXiv:2607.20321v1 Announce Type: cross Abstract: Neural surrogates are widely used in scientific machine learning for fast prediction of three-dimensional (3D) thermo-fluid fields. However, generating training data using conventional numerical solvers often incurs substantial co…

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报道来源 [1]

用于耦合热流体场的无标签有限体积残差注意力图神经网络训练

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