Sejin Kim

  • Course: Integrated MS-PhD Program
  • Research Topic: Application of reinforcement learning (RL) to computational fluid dynamics (CFD): fly-mimicking flyer control, airfoil design optimization, optimal grid generation for turbomachinery blades
  • Contact: ksj908 at postech.ac.kr
  • Member since March 2016

 

1. Publications

  • Jeon, Y., Park, J., Kim, S., Song, J. & You, D., “Numerical analysis of effects of gas turine combustor flow on a high pressure turbine stage” 한국전산유체공학회지, 25(4), 103-110, 2020.
  • Kim, S.*, Kim, I.*, & You, D. “Multi-condition multi-objective optimization using deep reinforcement learning.” Journal of Computational Physics, vol. 462, 111263, 2022

2. Presentations

  • Kim, S., Hong, S. & You, D., 2021. Control of a fly-inspired flyer in complex flow using computational fluid dynamics and reinforcement learning. Spring Meeting of the Korean Society for Computational Fluids Engineering, May 6-7, Online.
  • Kim, S., Hong, S. & You, D., 2021. Fly-scale air vehicle control using reinforcement learning and computational fluid-structural dynamics. Spring Meeting of the Division of Dynamics and Control, Korean Society of Mechanical Engineers, April 26-28, Buyeo, Korea.
  • Hong, S., Kim, S. & You, D., 2021. Mimicking and controlling the flight of a fly using computational fluid dynamics and reinforcement learning. Spring Meeting of the Division of Bioengineering, Korean Society of Mechanical Engineers, April 15-16, Online.
  • Kim, S. & You, D. “머신 러닝을 이용한 터보 기계 내 유동 해석의 자동화” The 10th National Congress on Fluids Engineering, August 22-25, 2018, Yeosu, Korea.
  • Kim, S. & You, D. “Automation of turbomachinery flow simulation using a reinforcement learning method” The Korean Society for Aeronautical and Space Sciences, November 28-December 1, 2018, Jeju, Korea.

 

3. Awards

 

4. Teaching (e.g. T.A.)

  • Fluid Mechanics (Undergraduate), Spring 2016