Abstract A finite element model of a tapered tensile specimen with a hardness transition zone in the gauge section and a varying width parameter is used for creating corresponding solution snapshots. Subsequently, a long short-term memory (LSTM) recurrent neural network (RNN) is trained on the selected snapshots, providing a parametrized solution model for a computationally efficient prediction of the structural response, allowing real-time model evaluation. In addition to a parametrized solution of the fracture localization, the model also captures the bifurcating local mesh deformation. The internal solution strategy of the RNN for predicting the bifurcation phenomenon is investigated and visualized.

中文翻译：

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基于神经网络的分叉结构断裂响应代理模型

摘要 一个锥形拉伸试样的有限元模型，在应变片截面有一个硬度过渡区，宽度参数变化，用于创建相应的解快照。随后，长短期记忆 (LSTM) 循环神经网络 (RNN) 在选定的快照上进行训练，为结构响应的计算有效预测提供参数化解决方案模型，从而允许实时模型评估。除了裂缝定位的参数化解决方案外，该模型还捕获分叉的局部网格变形。研究并可视化了用于预测分叉现象的 RNN 内部求解策略。