7 papers
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
Jack T. Beerman, Tyler J. Abele, Mehdi Taghizadeh +6
Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embedding differential operators direc…
High-fidelity simulations of shock initiation of an energetic crystal-binder system due to flyer impact
Shobhan Roy, Pradeep K. Seshadri, Chukwudubem Okafor +2
Meso-scale simulations of energy localization at hotspots provide closure models for multiscale frameworks of shock-to-detonation transition (SDT). Validation of such meso-scale ca…
Size is Not the Solution: Deformable Convolutions for Effective Physics Aware Deep Learning
Jack T. Beerman, Shobhan Roy, H. S. Udaykumar +1
Physics-aware deep learning (PADL) enables rapid prediction of complex physical systems, yet current convolutional neural network (CNN) architectures struggle with highly nonlinear…
Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)
Zoë J. Gray, Joseph B. Choi, Youngsoo Choi +3
Physics-aware deep learning (PADL) has gained popularity for use in complex spatiotemporal dynamics (field evolution) simulations, such as those that arise frequently in computatio…
Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows
Xinlun Cheng, Joseph Choi, H. S. Udaykumar +1
We present MRPARCv2, Multi-resolution Physics-Aware Recurrent Convolutional Neural Network, designed to model complex flows by embedding the structure of advection-diffusion-reacti…
A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
Xinlun Cheng, Bingzhe Chen, Joseph Choi +5
Modeling shock-to-detonation phenomena in energetic materials (EMs) requires capturing complex physical processes such as strong shocks, rapid changes in microstructural morphology…