7 citations · 12 across the 8 of their papers we have counts for
9 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…
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…
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…
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…
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…
ProtoryNet - Interpretable Text Classification Via Prototype Trajectories
Dat Hong, Tong Wang, Stephen S. Baek
We propose a novel interpretable deep neural network for text classification, called ProtoryNet, based on a new concept of prototype trajectories. Motivated by the prototype theory…