By coupling the elastic and crack surface energy in the total potential function, phase field methods favor a natural track of crack initiation and propagation within the continuum mechanics framework. However, in turn, it raises a critical issue in determining the coupling factor—energy degradation function. This work aims to tackle the problems induced by assigning a degradation function empirically, including the inaccurate reproduction of critical loads and unphysical stiffness reduction prior to fracture. Inspired by the concepts of data-driven computational mechanics, we propose a data-driven phase field scheme to enhance the numerical reproduction of physically consistent fracture responses. This method collaborates a datasets generator, a classifier, a physical-constrained learning algorithm, and the phase field solver in an extensible modular framework, allowing searching for the optimized damage constitutive relation in a carefully designed degradation function space with flexible form. In the case studies, mode I and mode II fracture characteristics of linear elastic and hyperelastic materials are well predicted by the data-driven phase field algorithm, which learns and derives the results from simple scalar datasets, controlling the average error lower than 10%. When introducing the input dataset noise, the model still shows robustness. The results demonstrate the model’s interpretability, thermodynamical consistency, and accuracy.
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