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		题名	作者	年代	出处	被引量
	1	Mapping microstructure to shock-induced temperature fields using deep learning显示文摘The response of materials to shock loading is important to planetary science,aerospace engineering,and energetic materials.Thermally activated processes,including chemical reactions and phase transitions,are significantly accelerated by energy localization into hotspots.These result from the interaction of the shockwave with the materials’microstructure and are governed by complex,coupled processes,including the collapse of porosity,interfacial friction,and localized plastic deformation.These mechanisms are not fully understood and the lack of models limits our ability to predict shock to detonation transition from chemistry and microstructure alone.We demonstrate that deep learning can be used to predict the resulting shock-induced temperature fields in composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input.The accuracy of the Microstructure-Informed Shock-induced Temperature net(MISTnet)model is higher than the current state of the art and its evaluation requires a fraction of the computation cost.	Chunyu Li Juan Carlos Verduzco Brian H.Lee Robert J.Appleton Alejandro Strachan	2023	npj Computational Materials2023，，1：	0
	2	An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials显示文摘The purpose of this work is the development of a trained artificial neural network for surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures.To this end,a U-Net-based convolutional neural network(CNN)is trained using results for the von Mises stress field from the numerical solution of initial-boundary-value problems(IBVPs)for mechanical equilibrium in such microstructures subject to quasi-static uniaxial extension.The resulting trained CNN(tCNN)accurately reproduces the von Mises stress field about 500 times faster than numerical solutions of the corresponding IBVP based on spectral methods.Application of the tCNN to test cases based on microstructure morphologies and boundary conditions not contained in the training dataset is also investigated and discussed.	Mohammad S.Khorrami Jaber R.Mianroodi Nima H.Siboni Pawan Goyal Bob Svendsen Peter Benner Dierk Raabe	2023	npj Computational Materials2023，，1：	0

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