This study presents a novel physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics. The linear relationships between the displacements of a material point and the displacements of its neighbours and the applied forces are obtained for the machine learning model by using linear regression. The numerical procedure for coupling the ordinary state-based peridynamic model and the machine learning model is also provided. The accuracy of the coupled model is verified by predicting deformations of a two-dimensional plate with circular cut-out subjected to tension and a two-dimensional representation of three points bending test. To further demonstrate the capabilities of the coupled model, damage predictions for a two-dimensional representation of a three-point bending test, a notched plate with a hole subjected to tension, a square plate with a pre-existing crack subjected to tension, and a plate with a pre-existing crack subjected to sudden loading are presented.

这项研究提出了一种基于普通基于状态的周边动力学的二维结构的新型物理学指导的机器学习模型。通过使用线性回归，可以为机器学习模型获得一个实体点的位移与其相邻节点的位移和所施加的力之间的线性关系。还提供了用于耦合基于状态的普通状态动力学模型和机器学习模型的数值过程。通过预测承受拉力的圆形切口二维板的变形和三点弯曲试验的二维表示，可以验证耦合模型的准确性。为了进一步证明耦合模型的功能，针对三点弯曲测试的二维表示的损伤预测，