Hamidreza Karbasian, Ph.D.

Assistant Professor in AI-Powered Digital Engineering Systems

Department

ME
Email

hkarbasian@smu.edu
Office Location

Embrey 301-J
Website

Hamidreza Karbasian is an Assistant Professor in AI-Powered Digital Engineering Systems in the Lyle School of Engineering (Department of Mechanical Engineering) at 91制片廠合集 (SMU). Before joining SMU, he was a Postdoctoral Research Associate in the Department of Mechanical Engineering at the Massachusetts Institute of Technology. Beside his academic careers, he also worked as the team lead of the aerodynamic group at Limosa Inc., where he led multiple projects regarding electric aircraft designs. Additionally, he was awarded the Fields CQAM postdoctoral fellowship at the Fields Institute at the University of Toronto. He also has previous experience as a postdoctoral fellow at Polytechnique Montreal, affiliated with the University of Montreal where he worked on digital twin and deep learning algorithms. His research work has resulted in over 30 publications in well-known journals and international conference presentations.

Education

PDF, Mechanical Engineering, Massachusetts Institute of Technology
PDF, Applied Mathematics, Fields Institute
Ph.D., Mechanical Engineering, Concordia University
M.Sc., Mechanical Engineering, Pusan National University

Research

  • Multidisciplinary Design Optimization (MDO).
  • Reduced Order Modeling.
  • Computational Fluid Dynamics.
  • Artificial Intelligence.

Publications

  • H.R. Karbasian, W.M. van Rees, A Deep-Learning Surrogate Model Approach for Optimization of Morphing Airfoils, AIAA SCITECH, 2023.
  • H.R. Karbasian, B.C. Vermeire, Application of physics-constrained data-driven reduced-order models to shape optimization, Journal of Fluid Mechanics, 2022.
  • H.R. Karbasian, B.C. Vermeire, Sensitivity analysis of chaotic dynamical systems using a physics constrained data-driven approach, Physics of Fluids, 2022.
  • H.R. Karbasian, J.A. Esfahani, A.M. Aliyu, K.C. Kim, Numerical analysis of wind turbines blade in deep dynamic stall, Renewable Energy, 2022.
  • H.R. Karbasian, B.C. Vermeire, Gradient-free aerodynamic shape optimization using Large Eddy Simulation, Computers and Fluids, 2021.
Hamidreza Karbasian, Ph.D.