FIP-GNN：用于可扩展预测晶粒级疲劳指示参数的图神经网络
FIP-GNN: Graph neural networks for scalable prediction of grain-level fatigue indicator parameters
AI总结：
提出图神经网络FIP-GNN，通过预测晶粒级疲劳指示参数并泛化至大体积微观结构，为高周疲劳建模提供高效且统计严谨的计算方案。
摘要：
高周疲劳是许多应用中结构合金的关键性能指标。实验疲劳测试的高成本、耗时和人力投入，使得人们亟需高效且准确的疲劳寿命计算机模型。我们提出了FIP-GNN——一种用于多晶体的图神经网络，它(i)将疲劳指示参数预测为晶粒级循环加载的非弹性响应，量化裂纹萌生的局部驱动力，并且(ii)将这些预测泛化到晶粒数量远超训练集的大型微观结构体积单元中。这些进展可为高周疲劳的统计严谨且计算高效的建模做出重大贡献，这是该领域长期存在的挑战。
英文摘要：
High-cycle fatigue is a critical performance metric of structural alloys for many applications. The high cost, time, and labor involved in experimental fatigue testing call for efficient and accurate computer models of fatigue life. We present FIP-GNN -- a graph neural network for polycrystals that (i) predicts fatigue indicator parameters as grain-level inelastic responses to cyclic loading quantifying the local driving force for crack initiation and (ii) generalizes these predictions to large microstructure volume elements with grain populations well beyond those used in training. These advances can make significant contributions to statistically rigorous and computationally efficient modeling of high-cycle fatigue -- a long-standing challenge in the field.
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