281 citations · 447 across the 4 of their papers we have counts for
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Prediction and control of two-dimensional decaying turbulence using generative adversarial networks
Jiyeon Kim, Junhyuk Kim, Changhoon Lee
With the recent rapid developments in machine learning (ML), several attempts have been made to apply ML methods to various fluid dynamics problems. However, the feasibility of ML…
Deep reinforcement learning for large-eddy simulation modeling in wall-bounded turbulence
Junhyuk Kim, Hyojin Kim, Jiyeon Kim +1
The development of a reliable subgrid-scale (SGS) model for large-eddy simulation (LES) is of great importance for many scientific and engineering applications. Recently, deep lear…
Unsupervised deep learning for super-resolution reconstruction of turbulence
Hyojin Kim, Junhyuk Kim, Sungjin Won +1
Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for training. This limitation…
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers
Junhyuk Kim, Changhoon Lee
A realistic inflow boundary condition is essential for successful simulation of the developing turbulent boundary layer or channel flows. Recent advances in artificial intelligence…