研究人员开发用于流固耦合预测的AI框架

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了一个新的机器学习框架，用于在变形网格上进行长时间流固耦合（FSI）预测。该系统集成了图神经网络算子和视觉Transformer用于流体动力学，以及长短期记忆网络用于结构运动。通过在界面处强制执行ALE一致的边界校正步骤来实现运动学兼容性，从而确保了准确性和稳定性。 AI

影响为复杂的物理模拟引入了一个新颖的框架，有可能提高流固耦合预测的准确性和稳定性。

排序理由这是一篇详细介绍针对特定科学问题的创新机器学习框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Shihang Zhao, Mart\'in Saravia, Haokui Jiang, Zhiyang Xue, Shunxiang Cao · 2026-05-05 04:00

一种ALE一致的图神经网络算子-Transformer框架用于流固耦合

arXiv:2605.00937v1 Announce Type: cross Abstract: We propose an arbitrary Lagrangian-Eulerian (ALE)-consistent machine learning framework for long-term fluid-structure interaction (FSI) prediction on deforming unstructured meshes. Specifically, the fluid dynamics are modeled by a…