爆炸与冲击2025，Vol.45Issue(5)：1-12,12.DOI:10.11883/bzycj-2024-0099
端到端机器学习代理模型构建及其在爆轰驱动问题中的应用
Construction of end-to-end machine learning surrogate model and its application in detonation driving problem
柏劲松 1刘洋 1陈翰 1钟敏1
作者信息
- 1. 中国工程物理研究院流体物理研究所冲击波物理与爆轰物理全国重点实验室,四川 绵阳 621999
- 折叠
摘要
Abstract
Artificial intelligence/machine learning methods can discover hidden physical patterns in data.By constructing an end-to-end surrogate model between state parameters and dynamic results,many complex engineering problems such as strong coupling,nonlinearity,and multiphysics can be efficiently solved.In the field of highly nonlinear explosion and shock dynamics,a classic detonation driving problem was chosen as the research object.Using numerical simulation results as training data for machine learning surrogate models,and combining forward simulation and reverse design organically.Based on deep neural network technology,an end-to-end surrogate model was constructed between feature position velocity profiles,material dynamic deformation,and engineering factors.And the calculation accuracy of the surrogate model was provided,verifying the ability to invert engineering factors from velocity profiles.The research results indicate that the end-to-end surrogate model has high predictive ability,with relative errors of less than 1%in both velocity profile prediction and engineering factor estimation.It can be applied to the rapid design,high-precision prediction,and agile iteration of highly nonlinear explosion and impact dynamics problems.关键词
计算爆炸力学/爆轰驱动/人工智能/机器学习/端到端代理模型/深度神经网络Key words
computational explosion mechanics/detonation drive/artificial intelligence/machine learning/end-to-end surrogate model/deep neural network分类
力学引用本文复制引用
柏劲松,刘洋,陈翰,钟敏..端到端机器学习代理模型构建及其在爆轰驱动问题中的应用[J].爆炸与冲击,2025,45(5):1-12,12.
