Creep‐Fatigue Life Prediction of 316H Stainless Steel through Physics‐Informed Data‐Driven Models

316H stainless steel is a critical material for fourth‐generation nuclear reactors, yet it is prone to creep‐fatigue failure under high‐temperature and high‐pressure conditions. This study evaluates physics‐driven models (including time fraction model, ductile exhaustion model, modified strain energy density exhaustion model, and plastic strain energy model) and data‐driven models (including support vector regression, random forests, generalized regression neural networks, and backpropagation neural networks) for predicting the creep‐fatigue life of 316H base metal and welded joints. On the basis of data‐driven models, physical information from the creep‐fatigue damage is further integrated to embed the physics‐informed input features and the physics‐informed loss function, thereby constructing physics‐informed data‐driven models to predict creep‐fatigue life. Results demonstrate that physics‐informed data‐driven models significantly outperform conventional approaches, with the physics‐informed generalized regression neural network achieving the highest accuracy ( R 2 = 0.9277). This work provides a robust framework for enhancing life prediction in high‐temperature structural applications.