| Learning finite difference methods for reaction-diffusion type equations with FCNN Y Kim, Y Choi Computers & Mathematics with Applications 123, 115-122, 2022 | 8 | 2022 |
| 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 | 7 | 2022 |
| Fast and accurate numerical solution of Allen–Cahn equation Y Kim, G Ryu, Y Choi Mathematical Problems in Engineering 2021, 1-12, 2021 | 6 | 2021 |
| Convolutional Autoencoders and Clustering for Low-dimensional Parametrization of Incompressible Flows J Heiland, Y Kim IFAC-PapersOnLine 55 (30), 430-435, 2022 | 1 | 2022 |
| 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 | 1 | 2021 |
| 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 | | 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 |
Yongho ChoiDaegu UniversityVerified email at daegu.ac.kr
