A convolutional deep neural network bridges molecular dynamics simulations to continuum finite element models for parameter-free prediction of shock-to-detonation in nanostructured energetic materials.
Ignition and Growth Modeling of Shock Initiation of the TATB-based Explosives LX-17 and PBX 9502 at Eight Initial Temperatures Spanning a 446K Range
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Microstructure-Aware Deep Learning Bridges Atomistics to Macroscale for Shock-to-Detonation Prediction
A convolutional deep neural network bridges molecular dynamics simulations to continuum finite element models for parameter-free prediction of shock-to-detonation in nanostructured energetic materials.