activity
20192026
most citedA Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation

124 citations · 251 across the 10 of their papers we have counts for

collaborators

10 papers

math.NA2026

Efficient Weak-Entropy PINN for Solving Hyperbolic Conservation Laws

Qi Gao, Kuang Huang, Xuan Di

In recent years, neural networks have significantly advanced numerical solutions of partial differential equations (PDEs). However, solving PDEs with discontinuous solutions, such…

math.NA2023★ 2 cited

Asymptotically compatibility of a class of numerical schemes for a nonlocal traffic flow model

Kuang Huang, Qiang Du

This paper considers numerical discretization of a nonlocal conservation law modeling vehicular traffic flows involving nonlocal inter-vehicle interactions. The nonlocal model invo…

math.AP2022★ 6 cited

A Space-time Nonlocal Traffic Flow Model: Relaxation Representation and Local Limit

Qiang Du, Kuang Huang, James Scott +1

We propose and study a nonlocal conservation law modelling traffic flow in the existence of inter-vehicle communication. It is assumed that the nonlocal information travels at a fi…

math.DS2021★ 6 cited

Discovering State Variables Hidden in Experimental Data

Boyuan Chen, Kuang Huang, Sunand Raghupathi +3

All physical laws are described as relationships between state variables that give a complete and non-redundant description of the relevant system dynamics. However, despite the pr…

cs.LG2021★ 124 cited

A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation

Rongye Shi, Zhaobin Mo, Kuang Huang +2

Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e.g., machine learning, ML), while each suffers from either deficient physics or small…

cs.LG2021★ 10 cited

Physics-Informed Deep Learning for Traffic State Estimation

Rongye Shi, Zhaobin Mo, Kuang Huang +2

Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., density) on road segments using partially observed data, plays an important role on efficient traffi…

math.OC2020★ 42 cited

Dynamic driving and routing games for autonomous vehicles on networks: A mean field game approach

Kuang Huang, Xu Chen, Xuan Di +1

This paper aims to answer the research question as to optimal design of decision-making processes for autonomous vehicles (AVs), including dynamical selection of driving velocity a…

math.AP2020★ 1 cited

Stability of a Nonlocal Traffic Flow Model for Connected Vehicles

Kuang Huang, Qiang Du

The emerging connected and automated vehicle technologies allow vehicles to perceive and process traffic information in a wide spatial range. Modeling nonlocal interactions between…

math.OC2019★ 18 cited

Stabilizing Traffic via Autonomous Vehicles: A Continuum Mean Field Game Approach

Kuang Huang, Xuan Di, Qiang Du +1

This paper presents scalable traffic stability analysis for both pure autonomous vehicle (AV) traffic and mixed traffic based on continuum traffic flow models. Human vehicles are m…

math.OC2019★ 42 cited

A Game-Theoretic Framework for Autonomous Vehicles Velocity Control: Bridging Microscopic Differential Games and Macroscopic Mean Field Games

Kuang Huang, Xuan Di, Qiang Du +1

This paper proposes an efficient computational framework for longitudinal velocity control of a large number of autonomous vehicles (AVs) and develops a traffic flow theory for AVs…