---
title: "无标签有限体积残差训练注意力图神经网络用于耦合热流体场"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.20321v1"
permalink: "https://feed.astralor.com/items/itm_8230ec0578f7434c7917fa0f"
published: "2026-07-22T16:07:09.000Z"
score: 77
channel: "ai"
tags: ["Graph Neural Network","无监督学习","有限体积法","热流体仿真","科学机器学习"]
---

# 无标签有限体积残差训练注意力图神经网络用于耦合热流体场

## AI 点评

> 这篇论文让我眼前一亮，它巧妙地将传统CFD中的有限体积法残差作为神经网络的损失函数，实现了真正的无监督学习，避免了昂贵的数据生成。对在工业界做仿真代理模型的工程师尤其重要，因为很多场景根本拿不到大量高保真标签数据。

## 速览

该论文提出一种无需标签数据的注意力图神经网络训练方法，利用有限体积法（FVM）残差作为损失函数，直接评估网格上的物理守恒方程。在稳态和瞬态热流体仿真任务中，该方法达到2.3-2.8%的全场归一化均方根误差，与CFD参考高度一致，并避免了传统数据监督方法所需的大量数据生成成本。结果表明FVM损失可成为神经代理模型实用的训练信号。

## 关键点

- 无需标注数据，利用有限体积法残差作为损失函数训练图神经网络。
- 在稳态基准测试中全场归一化均方根误差仅2.3-2.8%。
- 在参数化瞬态案例中精度超过有监督基线，且完全避免数据生成成本。

## 全文

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 computational and storage costs. We propose to train an attention graph neural network by minimizing the finite-volume method (FVM) residuals of the governing equations. These residuals are evaluated directly on the mesh, requiring no labeled data. We evaluate the trained surrogates against computational fluid dynamics (CFD) references and a data-supervised baseline across four scenarios. On the two steady-state benchmarks, the FVM-loss model achieves an all-field normalized root-mean-square error (nRMSE) of 2.3-2.8%. It demonstrates close agreement with the CFD references, including the buoyancy-energy coupling. On the two parametric transient cases, the FVM-loss model outperforms the supervised baseline in terms of accuracy, while avoiding the data-generation cost entirely. These results indicate that the FVM loss can provide a practical training signal for neural surrogates and reduce the model development cost.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_8230ec0578f7434c7917fa0f