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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 · [2 个来源] ·

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，特别是针对路径依赖的非弹性材料。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，在一次并行传递中预测应力轨迹。该框架利用因果掩码注意力机制来捕捉时间路径依赖性，并利用谱卷积进行离散化不变表示，从而能够准确预测塑性和损伤累积等复杂现象。 AI

影响 该框架可以实现更准确、更高效的材料行为模拟，有望加速材料发现和工程过程。

排序理由 该集群描述了一篇在arXiv上发表的研究论文，其中详细介绍了一种用于材料科学的新机器学习框架。

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新AI框架使用神经算子和因果注意力模拟材料行为

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该集群描述了一篇在arXiv上发表的研究论文，其中详细介绍了一种用于材料科学的新机器学习框架。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rishabh Arora, Lisa Scheunemann, Tim Brepols, Shahed Rezaei ·

    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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    通过神经算子和因果注意力学习材料的本构行为：塑性与损伤案例研究

    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 many practical settings, the relevant internal var…

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