Physics-informed neural network for creep-fatigue life prediction of Inconel 617 and interpretation of influencing factors

• Introduced physical constraints promote the stability of prediction performance and provides valuable guidance for engineering practices to build physics-informed neural network. • Introducing physical constraints effectively enhance the physical fundamentals of proposed physics-informed neural network. • The SHapley Additive exPlanations method and dependency analysis to reveal the intrinsic mechanism of the properties and structure of physics-informed neural network. Providing a comprehensive assessment of the creep-fatigue life of critical structures in nuclear power facilities is important for structural integrity and performance requirements during service. The data-driven modeling method can set aside traditional mechanical theory and use experimental data to drive creep-fatigue performance modeling directly, avoiding complex parameter fitting. A sufficiently robust purely data-driven model must be supported by a large amount of training data, which means significant time and financial costs. To address these issues, a physics-informed neural network (PINN) is proposed in this work to predict the creep-fatigue life of Inconel 617 at high temperatures, where the physical constraints are introduced to enhance the physical fundamentals. The prediction results show that introduced physical constraints promote the stability of prediction performance and analysis based on the strength of physical constraints provides valuable guidance for creep-fatigue modeling to select the suitable architecture for building PINN. The SHapley Additive exPlanations (SHAP) method and dependency analysis are utilized to reveal the intrinsic mechanism of the properties and structure of PINN.