Madhur Behl

About

Dr. Madhur Behl is an Associate Professor in the department of Computer Science at the University of Virginia and an Amazon Scholar. His research spans robotics, autonomous systems, and cyber-physical systems, with a focus on physical AI, autonomous driving, and high-speed decision making.

Research topics

• Computer Science
• Meteorology
• Geography
• Engineering
• Artificial Intelligence
• Environmental science
• Geology
• Machine Learning
• Cartography
• Oceanography

Selected publications

• Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

  Water Resources Research · 2020 · 181 citations

  Senior authorCorresponding

  Abstract Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D overland flow models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run times, they are often unsuitable for real‐time flood prediction at a street scale. This study explores the potential of a machine learning method, Random Fo…

  DOI

• Exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise

  Journal of Hydrology · 2020 · 89 citations

  DOI

• Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

  Journal of Hydroinformatics · 2020 · 73 citations

  Abstract Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of these systems can improve performance, and creating control policies for smart stormwater systems is an active area of study. This research explores reinforcement learning (RL) to create control policies to mitigate flood risk. RL is trained using a model of hypothetical urban catchments with a tid…

  DOI

• Estimating impacts of recurring flooding on roadway networks: a Norfolk, Virginia case study

  Natural Hazards · 2021 · 54 citations

  DOI

• Deep Dynamics: Vehicle Dynamics Modeling With a Physics-Constrained Neural Network for Autonomous Racing

  IEEE Robotics and Automation Letters · 2024-04-15 · 31 citations

  articleSenior author

  Autonomous racing is a critical research area for autonomous driving, presenting significant challenges in vehicle dynamics modeling, such as balancing model precision and computational efficiency at high speeds ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 280 km/h), where minor errors in modeling have severe consequences. Existing physics-based models for vehicle dynamics…

  PublisherDOI

Frequent coauthors

• Jonathan L. Goodall

  50 shared

• Rahul Mangharam

  36 shared

• Benjamin D. Bowes

  University of Virginia

  31 shared

• Mohamed M. Morsy

  State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering

  30 shared

• Jeffrey M. Sadler

  Oklahoma State University

  29 shared

• Truong X. Nghiem

  15 shared

• Faria Tuz Zahura

  Government of the United States of America

  8 shared

• Arsalan Heydarian

  Engineering Systems (United States)

  6 shared

Education

• Ph.D., Electrical and Systems Engineering

  University of Pennsylvania

  2015

• M.S., Electrical and Systems Engineering

  University of Pennsylvania

  2012

• B.S., ECE

  PEC University of Technology

  2009

Awards & honors

• NSF CAREER Award
• first place in the DOE Cleantech Prize (2016)
• winner of the World Embedded Software Contest (2011)
• multiple Outstanding Researcher Awards from the University o…

Similar researchers at University of Virginia

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• David Evansh-61
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