Publications
Journal Papers
Heiland, J.; Kim, Y.; Werner, S.W.R. (2024) Deep Polytopic Autoencoders for Low-dimensional Linear Parameter-varying Approximations and Nonlinear Feedback Design, under review
Kim, Y.; Im, C.; Mandl, T. (2024) Object Detection in Historical Images: Transfer Learning and Pseudo Labelling, under review
Heiland, J.; Kim, Y. (2024) Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows, under review
Heiland, J.; Kim, Y. (2023) Convolutional Autoencoders, Clustering, and POD for Low-dimensional Parametrization of Flow Equations, under reviewÂ
Kim, Y.; Choi, Y. (2022) "Learning Finite Difference Methods for Reaction-Diffusion Type Equations with FCNN", Comput. Math. Appl., Vol.123, pp.115-122
Im, C.; Kim, Y.; Mandl, T. (2022) Deep Learning for Historical books: Classification of Printing Technology for Digitized Images, Multi. Tools Appl., Vol.81, pp.5867-5888
Kim, Y.; Ryu, G.; Choi, Y. (2021) Fast and Accurate Numerical Solution of Allen-Cahn Equation, Math. Probl. Eng., Vol.2021, Article ID 5263989
Conferences
Heiland, J.; Kim, Y. (2024) Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models, 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics
Heiland, J.; Kim, Y. (2023) Autoencoders and POD for Very Low-dimensional Nonlinear Controller Models, SIAM Conference on CSE
Heiland, J.; Kim, Y. (2022) Convolutional Autoencoders and Clustering for Low-dimensional Parametrization of Incompressible Flows, Proceedings of the 25th International Symposium on MTNS, IFAC-PapersOnLine, Vol.55, pp.430-435
Kim, Y.; Mandl, T.; Im, C.; Schmidele, S.; Helm, W. (2021) Applying Computer Vision Systems to Historical Book Illustrations: Challenges and First Results, Post-Proceedings of the 5th Conference DHN, Vol.2865, pp.255-260
Kim, Y.; Lukashonak, H.; Tarepakdee, P.; Zavalich, K.; Arif, M. Ul Islam (2020) Disturbing Target Values for Neural Network Regularization, SRP Conference 2020, University of Hildesheim
Book
Kim, Y. (2022) Pytorch Basics for Deep Learning (Korean), Youngjin.com, ISBN 9788931465914 (earned recognition as an excellent book by KPIPA under the Ministry of Culture, Sports and Tourism)