Stebėti
Yongho Kim
Pavadinimas
Cituota
Cituota
Metai
Fast and accurate numerical solution of Allen–Cahn equation
Y Kim, G Ryu, Y Choi
Mathematical Problems in Engineering 2021 (1), 5263989, 2021
112021
Deep learning for historical books: classification of printing technology for digitized images
C Im, Y Kim, T Mandl
Multimedia Tools and Applications 81 (4), 5867-5888, 2022
102022
Learning finite difference methods for reaction-diffusion type equations with FCNN
Y Kim, Y Choi
Computers & Mathematics with Applications 123, 115-122, 2022
92022
Applying Computer Vision Systems to Historical Book Illustrations: Challenges and First Results.
Y Kim, T Mandl, C Im, S Schmideler, W Helm
DHN Post-Proceedings 2865, 255-260, 2021
22021
Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design
J Heiland, Y Kim, SWR Werner
arXiv preprint arXiv:2403.18044, 2024
12024
Convolutional Autoencoders and Clustering for Low-dimensional Parametrization of Incompressible Flows
J Heiland, Y Kim
IFAC-PapersOnLine 55 (30), 430-435, 2022
12022
Convolutional Autoencoders, Clustering, and Pod for Low-Dimensional Parametrization of Flow Equations
J Heiland, Y Kim
Computers & Mathematics with Applications 175, 49-61, 2024
2024
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows
J Heiland, Y Kim
arXiv preprint arXiv:2401.10620, 2024
2024
Going Deeper with Five-point Stencil Convolutions for Reaction-Diffusion Equations
Y Kim, Y Choi
arXiv preprint arXiv:2308.04735, 2023
2023
Disturbing Target Values for Neural Network Regularization
Y Kim, H Lukashonak, P Tarepakdee, K Zavalich, MI Arif
arXiv preprint arXiv:2110.05003, 2021
2021
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Straipsniai 1–10