To accurately predict the fatigue life of corroded steel wire, the authors proposed an artificial neural network (ANN) model and a probabilistic physics-guided neural network (PPgNN) model. Factors including stress range, mean stress, and corrosion rate were considered as input features of these two neural networks. The ANN model exhibited the best prediction accuracy with a determination coefficient of 0.94; however, it cannot capture the dispersion of fatigue life. Through introduction of physics constraints, PPgNN model was able to predict the standard deviation of fatigue life. The results showed that 94.79% of the dataset was within the 95% confidence interval.

中文翻译：

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人工神经网络中的耦合物理学以预测腐蚀钢丝的疲劳行为


为了准确预测腐蚀钢丝的疲劳寿命，作者提出了人工神经网络 （ANN） 模型和概率物理引导神经网络 （PPgNN） 模型。应力范围、平均应力和腐蚀速率等因素被认为是这两个神经网络的输入特征。ANN 模型表现出最佳的预测精度，决定系数为 0.94;但是，它无法捕获疲劳寿命的色散。通过引入物理约束，PPgNN 模型能够预测疲劳寿命的标准差。结果表明，94.79% 的数据集在 95% 置信区间内。