Predicting brittle fracture surface shape from a versatile database. (26th October 2018) - Huang, Yuhang; Yu, Yonghang; Kanai, Takashi
Abstract: In this paper, we propose a novel data‐driven method that uses a machine learning scheme for formulating fracture simulation with the boundary element method (BEM) as a regression problem. With this method, the crack opening displacement (COD) of every correlation node is predicted at the next frame. In our naive prediction, we design a feature vector directly exploiting stress intensities and toughness at the current frame so that our method predicts the COD at the next frame more reliably. Thus, there is no need to solve the original linear BEM system to calculate displacements. This enables us to propagate crack fronts using the estimated stress intensities. There are existing works that use the machine learning approach to accelerate the speed of traditional physics‐based simulations like smoke and fluid, but our work is the first to incorporate the machine learning scheme into BEM‐based fracture simulations. Our implementation accelerates the acquisition of displacements in linear time over the number of crack fronts at each time step compared with the conventional solution whose time complexity grows exponentially based on the BEM linear system. The databases generated by our method are versatile and can be applied to general situations and different models. Abstract : We propose a novel data‐driven method that uses a machine learning scheme for formulating fracture simulation with the boundary element method (BEM) as a regression problem. We design a feature vector directly exploiting stress intensities and toughness at the current frame. Our implementation accelerates the acquisition of displacements in linear time over the number of crack fronts at each time step. The databases generated by our method are versatile and can be applied to general situations and different models.