42 citations · 139 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
Joseph B. Choi, Phong C. H. Nguyen, Oishik Sen +2
Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven m…
A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials
Phong C. H. Nguyen, Yen-Thi Nguyen, Pradeep K. Seshadri +3
Predictive simulations of the shock-to-detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensiti…
Mechanisms of shock-induced initiation at micro-scale defects in energetic crystal-binder systems
Pratik Das, H. S. Udaykumar
Crystals of energetic materials, such as HMX, embedded in plastic binders are the building blocks of plastic-bonded explosives. Such heterogeneous energetic materials contain micro…
PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials
Phong C. H. Nguyen, Yen-Thi Nguyen, Joseph B. Choi +3
The thermo-mechanical response of shock-initiated energetic materials (EM) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructure in…