English(EN) Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage

新AI框架使用神经算子和因果注意力模拟材料行为

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了一种新颖的数据驱动框架来模拟材料的本构行为，特别关注表现出塑性和损伤的路径依赖性非弹性材料。该方法将变形材料视为从其应变历史到其应力响应的函数映射，在一次并行前向传递中预测完整的应力轨迹。该模型包含一个因果掩码注意力机制，以确保时间路径依赖性，同时保持计算并行性，以及用于离散化不变表示的谱卷积和用于处理非线性过渡的正弦激活函数。对多维材料模型的评估证明了对不可逆变形机制的准确预测和出色的并行效率。 AI

影响该框架可以实现更准确、更高效的工程和制造材料行为模拟。

排序理由该集群包含一篇详细介绍材料科学新AI方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 22 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍材料科学新AI方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Rishabh Arora, Lisa Scheunemann, Tim Brepols, Shahed Rezaei · 2026-09-03 04:00

通过神经算子和因果注意力学习材料的本构行为：以塑性和损伤为例

arXiv:2609.02194v1 Announce Type: new Abstract: Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated based on domain knowledge and calibrated against experimental data. However, in ma…

本文如何被排名

报道来源 [1]

通过神经算子和因果注意力学习材料的本构行为：以塑性和损伤为例

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