Physics-informed machine learning for low-cycle fatigue life prediction of 316 stainless steels

•Construct a neural network with physical constraints in loss function.•Effects of temperature and strain rate on fatigue life are involved.•The effect of the strength of physical constraints on prediction accuracy is discussed.•Prediction accuracy of fatigue life is located within the twice error band.