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A physics-informed neural network approach to fatigue life prediction using small quantity of samples
International Journal of Fatigue ( IF 6 ) Pub Date : 2022-09-17 , DOI: 10.1016/j.ijfatigue.2022.107270
Dong Chen, Yazhi Li, Ke Liu, Yi Li

A physics-informed neural network (PINN) is proposed for fatigue life prediction with small amount of experimental data enhanced by physical models describing the fatigue behavior of materials. A multi-fidelity network architecture is constructed to accommodate the mixed data with different fidelities by embedding the physical models into the hidden neuron as the activation functions. Experimental data of two metallic materials is collected for the validation. The results show that the proposed PINN produced physically consistent and accurate results, and performed well in the extrapolative fatigue life prediction.



中文翻译:

一种基于物理信息的神经网络方法,使用少量样本进行疲劳寿命预测

提出了一种物理信息神经网络 (PINN) 用于疲劳寿命预测,其中少量实验数据由描述材料疲劳行为的物理模型增强。通过将物理模型作为激活函数嵌入到隐藏神经元中,构建了一个多保真网络架构,以适应具有不同保真度的混合数据。收集两种金属材料的实验数据进行验证。结果表明,所提出的 PINN 产生了物理上一致且准确的结果,并且在外推疲劳寿命预测中表现良好。

更新日期:2022-09-17
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