54 citations · 58 across the 4 of their papers we have counts for
4 papers
TPTNet: A Data-Driven Temperature Prediction Model Based on Turbulent Potential Temperature
Jun Park, Changhoon Lee
A data-driven model for predicting the surface temperature using neural networks was proposed to alleviate the computational burden of numerical weather prediction (NWP). Our model…
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…
Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss
Jongsu Kim, Changhoon Lee
Forecasting the particulate matter (PM) concentration in South Korea has become urgently necessary owing to its strong negative impact on human life. In most statistical or machine…