5 citations · 11 across the 5 of their papers we have counts for
5 papers
Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
Archie J. Huang, Dongdong Wang, Shaurya Agarwal +3
Physics-informed deep learning (PIDL) neural networks have shown their capability as a useful instrument for transportation practitioners in utilizing the underlying relationship b…
Benchmarking the accuracy of superconducting pair-pair correlations within Constrained Path Quantum Monte Carlo
Jodie Roberts, Beau A. Thompson, R. Torsten Clay
Ground state properties of the Hubbard model are of fundamental importance to understand the mechanism of unconventional superconductivity in the high-T_c cuprates and other materi…
Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural Networks
Archie J. Huang, Animesh Biswas, Shaurya Agarwal
This research contributes to the advancement of traffic state estimation methods by leveraging the benefits of the nonlocal LWR model within a physics-informed deep learning framew…
On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models
Archie J. Huang, Shaurya Agarwal
Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing popularity in understanding the evolution of systems governed by physical laws in terms o…
Physics Informed Deep Learning: Applications in Transportation
Archie J. Huang, Shaurya Agarwal
A recent development in machine learning - physics-informed deep learning (PIDL) - presents unique advantages in transportation applications such as traffic state estimation. Conso…