Machine learning aided phase field method for fracture mechanics

A machine learning aided non-deterministic damage prediction framework against both 2D and 3D fracture problems is presented in this paper. By introducing a newly developed extended support vector regression (X-SVR) with generalized Dirichlet feature mapping into the phase field crack growth model, a damage assessment method that contains both crack diagnosis and prognosis is designed. Within the proposed analysis framework, the intricate fracture mechanism of practical engineering system can be learnt by the X-SVR model so a continuous damage diagnosis-prognosis loop can be established to assess the latest working condition of the structure. The proposed framework is applicable not only for quantifying and then assessing the current working conditions, but also for predicting the potentially crack propagation against the future forecasted information. Compared with the established experimental records and numerical result, the accuracy, effectiveness, and computational efficiency of the proposed framework are fully verified.