326 citations · 450 across the 9 of their papers we have counts for
9 papers
Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness -- a data driven approach
Jiasheng Yang, Alexander Stroh, Sangseung Lee +3
The purpose of the present work is to examine two possibilities; firstly, predicting equivalent sand-grain roughness size based on the roughness height probability density fu…
Effects of spatiotemporal correlations in wind data on neural network-based wind predictions
Heesoo Shin, Mario Rüttgers, Sangseung Lee
This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that inclu…
Predicting drag on rough surfaces by transfer learning of empirical correlations
Sangseung Lee, Jiasheng Yang, Pourya Forooghi +2
Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a large high-fidelity data…
Mechanisms of a Convolutional Neural Network for Learning Three-dimensional Unsteady Wake Flow
Sangseung Lee, Donghyun You
Convolutional neural networks (CNNs) have recently been applied to predict or model fluid dynamics. However, mechanisms of CNNs for learning fluid dynamics are still not well under…
Prediction of typhoon tracks using a generative adversarial network with observational and meteorological data
Mario Rüttgers, Sangseung Lee, Donghyun You
Tracks of typhoons are predicted using a generative adversarial network (GAN) with observational data in form of satellite images and meteorological data from a reanalysis database…
Deep learning approach in multi-scale prediction of turbulent mixing-layer
Jinu Lee, Sangseung Lee, Donghyun You
Achievement of solutions in Navier-Stokes equation is one of challenging quests, especially for its closure problem. For achievement of particular solutions, there are variety of n…
Typhoon track prediction using satellite images in a Generative Adversarial Network
Mario Rüttgers, Sangseung Lee, Donghyun You
Tracks of typhoons are predicted using satellite images as input for a Generative Adversarial Network (GAN). The satellite images have time gaps of 6 hours and are marked with a re…
Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning
Sangseung Lee, Donghyun You
Unsteady flow fields over a circular cylinder are trained and predicted using four different deep learning networks: convolutional neural networks with and without consideration of…
Prediction of laminar vortex shedding over a cylinder using deep learning
Sangseung Lee, Donghyun You
Unsteady laminar vortex shedding over a circular cylinder is predicted using a deep learning technique, a generative adversarial network (GAN), with a particular emphasis on elucid…