1 citations · 2 across the 2 of their papers we have counts for
4 papers
Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs
Seungchan Ko, Seok-Bae Yun, Youngjoon Hong
In this paper, we perform the convergence analysis of unsupervised Legendre--Galerkin neural networks (ULGNet), a deep-learning-based numerical method for solving partial different…
Invertible Monotone Operators for Normalizing Flows
Byeongkeun Ahn, Chiyoon Kim, Youngjoon Hong +1
Normalizing flows model probability distributions by learning invertible transformations that transfer a simple distribution into complex distributions. Since the architecture of R…
Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation
Bryce Chudomelka, Youngjoon Hong, Hyunwoo Kim +1
Nonlinear differential equations are challenging to solve numerically and are important to understanding the dynamics of many physical systems. Deep neural networks have been appli…
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods
Byungjoo Kim, Bryce Chudomelka, Jinyoung Park +3
Deep neural networks have achieved state-of-the-art performance in a variety of fields. Recent works observe that a class of widely used neural networks can be viewed as the Euler…