To address the limitations that the traditional fatigue design for wind turbine towers using deterministic S-N curves might not accurately quantify fatigue life dispersion， a probabilistic fatigue life prediction method was proposed based on physics-informed neural networks. By embedding the physical prior knowledge such as fatigue life dispersion， monotonicity and nonlinearity into the neural networks， a probabilistic prediction model capable of accurately quantifying uncertainty was constructed. Compared with traditional methods， the proposed method reduces the normalized root mean square error（NRMSE） by up to 31.58%. A 16 MW wind turbine simulation model was established in accordance with IEC standards， and tower load data were obtained by using Bladed software. Combined with wind-speed distribution， rain flow counting and the Miner rule， the probabilistic fatigue life prediction of the towers was achieved. The results show that the proposed method effectively characterizes the probabilistic features of fatigue damages， and the tower lifetime varies significantly with reliability requirements （shortening from 83.3 years at 50% probability to 18.2 years at 99.9% probability）， which provides a reliable basis for probabilistic fatigue design and safety assessment of wind turbine towers.
