120 citations · 229 across the 12 of their papers we have counts for
13 papers
Attention Is All You Need (to Avoid Spurious Oscillations)
Jinyoung Jeong, Joseph B. Choi, Xinlun Cheng +3
Can attention move a shock across several cells in one update without breaking it? We develop a conservative, fixed grid finite-volume scheme in which a CFL-conditioned attention f…
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…
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…
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…
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…
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…
NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks
Sehyun Chun, Nima Hamidi Ghalehjegh, Joseph B. Choi +4
A new convolutional neural network (CNN) architecture for 2D driver/passenger pose estimation and seat belt detection is proposed in this paper. The new architecture is more nimble…