Physics-informed neural network for fatigue life prediction of irradiated austenitic and ferritic/martensitic steels

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 experience cyclic loading and irradiation at elevated temperatures, causing complex degradation that traditional empirical models fail to capture accurately. The developed PINN model incorporates physical fatigue life constraints into its loss function, improving prediction accuracy and generalizability. Trained on 495 data points, including both irradiated and unirradiated conditions, the model outperforms traditional machine learning models like Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and the conventional Neural Network. SHapley Additive exPlanations analysis identifies strain amplitude, irradiation dose, and testing temperature as dominant features, each inversely correlated with fatigue life, consistent with physical understanding. PINN captures saturation behaviour in fatigue life at higher strain amplitudes in F/M steels. Overall, the PINN framework offers a reliable and interpretable approach for predicting fatigue life in irradiated alloys, enabling informed alloy selection.