AMIRHOSSEIN ARZANI portrait
  • Assistant Professor, scientific computing and imaging institute
  • Assistant Professor, Mechanical Engineering

Research Keywords

  • Scientific Machine learning
  • Mass transport
  • Dynamical Systems
  • Data-driven reduced-order modeling
  • Computational structural mechanics
  • Computational fluid dynamics (CFD)
  • Cardiovascular biomechanics

Presentations

  • Csala, H., Arzani, A., “Enhancing corrupt cardiovascular flow data with machine learning”, Summer Biomechanics, Bioengineering and Biotransport Conference, Vail, CO, June 4-8, 2023. Conference Paper, Refereed, Presented, 06/2023.
  • Flow physics and data-driven near-wall transport in blood flow. Toronto Interdisciplinary Modeling, Biomechanics, and Imaging Team seminar series, Ryerson University, Virtual, March 2023. Invited Talk/Keynote, Presented, 03/2023.
  • Flow physics and data-driven near-wall transport in blood flow. Advanced Modeling & Simulations seminar series, University of Texas El-Paso, Virtual, Feb 2023. Invited Talk/Keynote, Presented, 02/2023.
  • Scientific machine learning for modeling near-wall mass transport and boundary layers. CRUNCH seminar series, Brown University, Virtual, Jan 2023. Invited Talk/Keynote, Presented, 01/2023.
  • Aliakbari, M., Vadasz, P., Arzani, A., “Transfer learning enhanced physics-informed neural networks for forward and inverse transport problems in heterogeneous domains”, APS Division of Fluid Dynamics Annual Meeting, Indianapolis, IN, Nov. 20–22, 2022. Other, Presented, 11/2022.
  • Csala, H., Dawson, S., Arzani, A., “Manifold learning and deep autoencoders for nonlinear embedding of unsteady fluid flows”, APS Division of Fluid Dynamics Annual Meeting, Indianapolis, IN, Nov. 20–22, 2022. Other, Presented, 11/2022.
  • Arzani, A., Cassel, K. W., D’Souza, R. M., ‘Solving thin boundary layer problems with physics-informed machine learning inspired by perturbation theory”, APS Division of Fluid Dynamics Annual Meeting, Indianapolis, IN, Nov. 20–22, 2022. Other, Presented, 11/2022.

Research Groups

  • Alexis Throop, Graduate Student. 08/2023 - present.
  • Mojgan Alishiri, Graduate Student. 08/2023 - present.
  • Nathan Sudbury, Undergraduate Student. 01/2023 - present.
  • Steven LaBelle, Postdoc. 01/2023 - present.
  • Bray Moll, Graduate Student. 08/2022 - present.