activity
20202025
most citedDeep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

120 citations · 185 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025★ 1 cited

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…

physics.comp-ph2025

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material

Shahab Azarfar, Joseph B. Choi, Phong CH. Nguyen +4

Coupling of physics across length and time scales plays an important role in the response of microstructured materials to external loads. In a multi-scale framework, unresolved (su…

cs.LG2024★ 3 cited

PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling

Phong C. H. Nguyen, Xinlun Cheng, Shahab Azarfar +6

Modeling unsteady, fast transient, and advection-dominated physics problems is a pressing challenge for physics-aware deep learning (PADL). The physics of complex systems is govern…

cond-mat.mtrl-sci2022★ 30 cited

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…

cond-mat.mtrl-sci2022★ 31 cited

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…

cond-mat.mtrl-sci2020★ 120 cited

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

Sehyun Chun, Sidhartha Roy, Yen Thi Nguyen +3

The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reacti…