爆炸与冲击2026，Vol.46Issue(5)：46-57,12.DOI:10.11883/bzycj-2025-0324
单晶金属中微孔洞生长过程的深度学习预测方法
A deep learning prediction method for growth of micro voids in single-crystal metal
摘要
Abstract
A novel deep neural network was proposed to predict the growth of micro voids in single-crystal metal based on U-Net and Transformer in this paper.The dataset was constructed through molecular dynamics(MD)simulation results of a single-crystal copper atom model with initial double ellipsoidal voids.A data preprocessing scheme based on background mesh was proposed to perform local statistics on the simulation results.The information obtained from simulation results,such as void morphology,dislocation distribution,and von Mises effective stress,was converted into local statistics on the background mesh.These statistics were then converted into pixel matrix format as the input of the deep neural network.Multiple data samples can be generated from the results of one single MD simulation,which significantly reduces the computational resources required for dataset generation.The samples encompass typical stages of the void growth,which enables the network to capture key features and to facilitate data augmentation conveniently.The deep neural network model consists of four parts:U-Net composed of down-sampling and up-sampling networks,a generation model,a Query network model,and a regression prediction network.The model input includes both physical information and positional information.The output is the predicted physical information for the next time step.The loss function is a superposition of loss functions for each predicted variable.Numerical examples demonstrate that the aforementioned deep-learning method can accurately predict the global porosity ratio,dislocation density,and von Mises stress during growth of micro voids in single-crystal metal.The time for the network prediction can reach two orders of magnitude lower than that of MD simulation.关键词
深度学习/微孔洞生长/分子动力学/U-Net/TransformerKey words
deep learning/micro void growth/molecular dynamics/U-Net/Transformer分类
数理科学引用本文复制引用
苏浩,赵雷洋,丛龙跃,陈聪,关添元,刘岩..单晶金属中微孔洞生长过程的深度学习预测方法[J].爆炸与冲击,2026,46(5):46-57,12.基金项目
国家自然科学基金(12572228) （12572228）
