PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials.

ABSTRACT: The thermo-mechanical response of shock-initiated energetic materials (EMs) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructures in a "materials-by-design" framework. However, the current design practice is limited, as a large ensemble of simulations is required to construct the complex EM structure-property-performance linkages. We present the physics-aware recurrent convolutional (PARC) neural network, a deep learning algorithm capable of learning the mesoscale thermo-mechanics of EM from a modest number of high-resolution direct numerical simulations (DNS). Validation results demonstrated that PARC could predict the themo-mechanical response of shocked EMs with comparable accuracy to DNS but with notably less computation time. The physi

SUBMITTER: Nguyen PCH 

PROVIDER: S-EPMC10146890 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature