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

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中图分类号：0389 国标学科代码：13035 文献标志码：A

Abstract： Artificial intellgence/machine learming methods candiscover hidden physical patters in data.Byconstructing an end-to-end surogate model between state parameters and dynamic results， many complex engineering problems such as strong coupling，nonlinearity，and multiphysicscan be eficiently solved.Inthe fieldof highlynonlinearexplosion and shock dynamics，a clasic detonation driving problem was chosen asthe research object.Using numerical simulation results as trainingdatafor machine learningsurrogate models，and combining forward simulation and reversedesign organicall. Based on deepneural network technology，anend-to-end surogatemodel wasconstructed between feature position velocity profiles， material dynamic deformation，and engineering factors.And the calculation accuracyof the surrogate model was provided， verifyingtheabilitytoinvertengineering factorsfromvelocityprofiles.Theresearchresultsindicatethattheend-to-end surrogate model has high predictive ability，with relative errors of less than 1 % in both velocity profile prediction and enginering factorestimation.Itcanbeappliedtotherapiddesign，high-precisionprediction，andagileiterationof highly nonlinear explosion and impact dynamics problems.

Keywords：computational explosionmechanics； detonation drive；artificial inteligence； machine learning； end-to-end surrogatemodel； deep neural network

人工智能（artificial inteligence，AI）是能够和人一样进行感知、认知、决策和执行的人工程序或系统，是新一轮科技革命和产业变革的重要驱动力量。（剩余12596字）