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.

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