English(EN) Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries

新的Cova-PINN框架增强了复杂几何形状中的传热模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了Cova-PINN，一种新颖的多域物理信息神经网络（PINN）框架，旨在提高复杂几何形状中流固共轭传热（CHT）模拟的准确性。与以往分别处理域的方法不同，Cova-PINN在局部和全局尺度上优化了跨域能量平衡。在各种换热器设计上的评估表明，与现有的PINN基线相比，Cova-PINN显著降低了出口温度和整体能量传递的误差。 AI

影响提高了复杂工程问题的模拟精度，可能加速热管理中的设计周期。

排序理由详细介绍物理信息神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Weizheng Zhang, Xunjie Xie, Hao Pan, Lin Lu · 2026-10-09 04:00

Cova-PINN：复杂几何体中流固共轭传热的跨域守恒物理信息神经网络

arXiv:2610.11108v1 Announce Type: new Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT). However, standard multi-domain PINNs enforce governing equations and interface…

本文如何被排名

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Cova-PINN：复杂几何体中流固共轭传热的跨域守恒物理信息神经网络

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