English(EN) A variational physics-informed graph neural network for heterogeneous solid mechanics

新的PI-GNN提供改进的应力局部化建模

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种新颖的变分物理信息图神经网络（PI-GNN），旨在更准确地模拟非均质固体中的应力局部化。与依赖惩罚项或特定正则化宽度的传统物理信息神经网络（PINNs）不同，该PI-GNN将非均质性直接集成到网格图中并最小化总势能。这种方法显著减少了应力和位移计算中的误差，尤其是在材料刚度失配的宽范围内，其性能优于强形式PINNs，并取得了与有限元方法相当的结果。 AI

影响这种PI-GNN方法可以提高固体力学模拟的准确性和效率，可能对材料科学和工程设计等领域产生影响。

排序理由详细介绍计算力学新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Aashay Rajan Yadav, Amiya Prakash Das, Ratna Kumar Annabattula · 2026-09-11 04:00

用于异构固体力学的变分物理信息图神经网络

arXiv:2609.10983v1 Announce Type: cross Abstract: Stress localization in heterogeneous solids is governed by the bimaterial interface, where the displacement field remains $C^0$-continuous, while in-plane stresses jump due to the stiffness mismatch. Coordinate-based physics-infor…

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用于异构固体力学的变分物理信息图神经网络

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