66 citations · 108 across the 5 of their papers we have counts for
5 papers
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Sunwoong Yang, Hojin Kim, Yoonpyo Hong +3
This study explores the potential of physics-informed neural networks (PINNs) for the realization of digital twins (DT) from various perspectives. First, various adaptive sampling…
Compact and Intuitive Airfoil Parameterization Method through Physics-aware Variational Autoencoder
Yu-Eop Kang, Dawoon Lee, Kwanjung Yee
Airfoil shape optimization plays a critical role in the design of high-performance aircraft. However, the high-dimensional nature of airfoil representation causes the challenging p…
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task
Sunwoong Yang, Kwanjung Yee
This study aims to comprehensively investigate the deep ensemble approach, an approximate Bayesian inference, in the multi-output regression task for predicting the aerodynamic per…
Physics-aware Reduced-order Modeling of Transonic Flow via -Variational Autoencoder
Yu-Eop Kang, Sunwoong Yang, Kwanjung Yee
Autoencoder-based reduced-order modeling (ROM) has recently attracted significant attention, owing to its ability to capture underlying nonlinear features. However, two critical dr…
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil
Sunwoong Yang, Sanga Lee, Kwanjung Yee
The inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is prespecified. However, it has some significant limitations…