Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Yee, Kwanjung"'
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 approaches for collocation points are investigated to verify their eff
Externí odkaz:
http://arxiv.org/abs/2401.08667
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 problem known as the "curse of dimensionality". To overcome this proble
Externí odkaz:
http://arxiv.org/abs/2311.10921
Autor:
Yang, Sunwoong, Yee, Kwanjung
This study aims to comprehensively investigate the deep ensemble approach, an approximate Bayesian inference, in the multi-output regression task for predicting the aerodynamic performance of a missile configuration. To this end, the effect of the nu
Externí odkaz:
http://arxiv.org/abs/2303.16210
Autoencoder-based reduced-order modeling (ROM) has recently attracted significant attention, owing to its ability to capture underlying nonlinear features. However, two critical drawbacks severely undermine its scalability to various physical applica
Externí odkaz:
http://arxiv.org/abs/2205.00608
Publikováno v:
In Aerospace Science and Technology September 2024 152
Autor:
Kim, Yonghwan, Yee, Kwanjung
Publikováno v:
In International Journal of Heat and Mass Transfer 1 September 2024 229
Publikováno v:
In Chinese Journal of Aeronautics August 2024 37(8):166-189
Publikováno v:
In Applied Energy 1 January 2025 377 Part B
The inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is prespecified. However, it has some significant limitations that prevent it from achieving full efficiency. First, the iterative
Externí odkaz:
http://arxiv.org/abs/2108.08500
Autor:
Yang, Sunwoong, Yee, Kwanjung
Publikováno v:
In Engineering Applications of Artificial Intelligence June 2024 132