5 citations · 11 across the 10 of their papers we have counts for
11 papers
Magnetically Charged Black Holes from Grand Unified Theory
Tanmay Vachaspati, Anja Wachowitz
Grand Unified models of particle physics inevitably give rise to magnetic monopoles. Often the model will also contain a weakly broken discrete symmetry that leads to the formation…
Trust as a Field: A Macroscopic Representation for Vehicular Networks
Md Mahmudul Islam, Shaurya Agarwal
Trust assessment is a fundamental component of cooperative and connected vehicle systems. However, existing approaches operate primarily at the level of individual vehicles, making…
Time-varying System Identification of Bedform Dynamics Using Modal Decomposition
Shakib Mustavee, Arvind Singh, Shaurya Agarwal
Measuring sediment transport in riverbeds has long been a challenging research problem in geomorphology and river engineering. Traditional approaches rely on direct measurements us…
A Stochastic Differential Equation Framework for Modeling Queue Length Dynamics Inspired by Self-Similarity
Shakib Mustavee, Shaurya Agarwal, Arvind Singh
This article develops a stochastic differential equation (SDE) for modeling the temporal evolution of queue length dynamics at signalized intersections. Inspired by the observed qu…
DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications
Muhammad Shahbaz, Shaurya Agarwal
Recent advancements in deep-learning methods for object detection in point-cloud data have enabled numerous roadside applications, fostering improvements in transportation safety a…
Self-Similar Characteristics in Queue Length Dynamics: Insights from Adaptive Signalized Corridor
Shakib Mustavee, Shaurya Agarwal
Self-similarity, a fractal characteristic of traffic flow dynamics, is widely recognized in transportation engineering and physics. However, its practical application in real-world…
An Extreme Value Theory Approach for Understanding Queue Length Dynamics in Adaptive Corridors
Shakib Mustavee, Pushkin Kachroo, Shaurya Agarwal
This paper introduces a novel approach employing extreme value theory to analyze queue lengths within a corridor controlled by adaptive controllers. We consider the maximum queue l…
Existence of Trust-field in Vehicular Ad Hoc Networks: Empirical Evidence
Md Mahmudul Islam, Shaurya Agarwal
Vehicular Ad Hoc Networks (VANETs) play a crucial role in enhancing road safety and traffic efficiency by enabling communication between vehicles (V2V) and between vehicles and inf…
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…