This study proposes a Physics-Informed Neural Network (PINN) framework to predict the low-cycle fatigue (LCF) life of irradiated austenitic and ferritic/martensitic (F/M) steels used in nuclear reactors. These materials undergo cyclic loading, neutron irradiation, and elevated temperatures, leading to complex degradation mechanisms that are difficult to capture with conventional empirical or purely data-driven models. The proposed PINN embeds fatigue-life governing physical constraints into the loss function, enabling physically consistent learning while improving predictive accuracy, reliability, and generalizability. The model was trained on 495 strain-controlled fatigue data points spanning irradiated and unirradiated conditions. Compared with traditional machine learning approaches, including Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and conventional neural networks, the PINN demonstrated superior performance. SHapley Additive exPlanations (SHAP) analysis identified strain amplitude, irradiation dose, and test temperature as the dominant features, each exhibiting physically meaningful inverse correlations with fatigue life. Univariate and multivariate analyses revealed clear alloy-specific degradation characteristics. Austenitic steels exhibited strong nonlinear coupling among strain amplitude, irradiation dose, and temperature, resulting in pronounced fatigue degradation under combined loading. In contrast, F/M steels demonstrated comparatively stable irradiation responses, including dose-saturation behavior, but showed sensitivity to elevated temperatures beyond tempering thresholds. Overall, the proposed PINN framework serves as a reliable and interpretable tool for reactor-relevant fatigue assessment, enabling performance evaluation for advanced nuclear applications.

翻译：本研究提出了一种基于物理信息的神经网络（PINN）框架，用于预测核反应堆中辐照奥氏体及铁素体/马氏体（F/M）钢的低周疲劳（LCF）寿命。这些材料在循环载荷、中子辐照及高温环境下服役，会产生复杂的退化机理，难以通过传统经验模型或纯数据驱动模型准确捕捉。所提出的PINN将疲劳寿命的物理约束嵌入损失函数，在提升预测精度、可靠性和泛化能力的同时，实现物理一致性的学习。模型基于495个涵盖辐照与未辐照工况下的应变控制疲劳数据点进行训练。与传统机器学习方法（包括随机森林、梯度提升、极限梯度提升及常规神经网络）相比，PINN展现出更优性能。通过SHAP（沙普利加性解释）分析发现，应变幅值、辐照剂量和试验温度是主导特征，且各特征与疲劳寿命均呈具有物理意义的反相关性。单变量与多变量分析揭示了不同合金特有的退化特征：奥氏体钢在应变幅值、辐照剂量与温度间呈现强非线性耦合效应，导致联合加载下疲劳性能显著退化；而F/M钢则展现出相对稳定的辐照响应特性（包括剂量饱和行为），但对超过回火阈值的温度升高较为敏感。总体而言，所提出的PINN框架可作为反应堆相关疲劳评估的可靠且可解释工具，为先进核应用提供性能评价支持。