5 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
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…
cs.LG2023★ 5 cited
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…
cs.LG2023★ 4 cited
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…