Rikhi Bose

Rikhi Bose

Assistant Professor
rikhi.bose@mahindrauniversity.edu.in

Dr. Rikhi Bose is an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Ecole Centrale School of Engineering, Mahindra University. His general expertise as a researcher is in the areas of Computational Fluid Dynamics and scientific Machine/ Deep Learning. His interests and expertise are in the areas of flow stability, laminar to turbulence transition, complex effects in turbulent flows, convection dominated flows, and turbulence closure modeling using data-driven methods.

Ph.D.

  • Ph.D. Aerospace Engineering Iowa State University (2016)

M.Tech.

  • M.Tech. Aerospace Engineering. IIT Kanpur (2012)

B.E.

  • B.E. Mechanical Engineering. Jadavpur University (2009)

Postdoc

  • Postdoc, Max Planck Institute for Solar System Research 2023-2024 NIST Director’s Postdoctoral Fellow, National Institute for Standards & Technology (2018-2022)

Postdoc

  • Postdoc, Johns Hopkins University (2017-2018)

2024

  • Rikhi Bose, Veeraraghavan Kannan, Xiaojue Zhu. The generalized quasilinear approximation of two-dimensional Rayleigh-B ́enard convection, J. Fluid Mech., 998, A52, 2024. IF: 3.8
  • Rikhi Bose, Paul A. Durbin. Mixed mode transition in boundary layers: Helical instability, Phys. Rev. Fluids, 9, 063905, 2024. IF: 2.5
  • Rikhi Bose, Arunabha Roy. Invariance embedded physics-infused deep neural network-based sub-grid scale models for turbulent flows, Eng. Appl. Artif. Intell., 128, 107483, 2024. IF: 7.5

2023

  • Arunabha Roy, Rikhi Bose, Veera Sundararaghavan, Raymundo Arróyave. Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity, Neural Netw., 162, 472-489, 2023. IF: 6.
  • Rikhi Bose, Adam Pintar, Emil Simiu. Simulation of Atlantic Hurricane Tracks and Features: A Coupled Machine Learning Approach, Artif. Intell. Earth Syst., 2(2), 220060, 2023. IF: --
  • Laminar to turbulence transition in boundary layers
  • Deep Learning closure models for turbulence
  • Large superstructures in convection dominated flows