42 citations · 128 across the 9 of their papers we have counts for
9 papers
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…
Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements
Hiep Vo Dang, Phong C. H. Nguyen
Reconstructing high-fidelity fluid flow fields from sparse sensor measurements is vital for many science and engineering applications but remains challenging because of dimensional…
FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow Field From Limited Sensor Measurements
Phong C. H. Nguyen, Joseph B. Choi, Quang-Trung Luu
Many applications in computational and experimental fluid mechanics require effective methods for reconstructing the flow fields from limited sensor data. However, this task remain…
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…
Convolutional neural networks for large-scale dynamical modeling of itinerant magnets
Xinlun Cheng, Sheng Zhang, Phong C. H. Nguyen +3
Complex spin textures in itinerant electron magnets hold promises for next-generation memory and information technology. The long-ranged and often frustrated electron-mediated spin…
Challenges and opportunities for machine learning in multiscale computational modeling
Phong C. H. Nguyen, Joseph B. Choi, H. S. Udaykumar +1
Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous…
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…
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…