27 citations · 75 across the 17 of their papers we have counts for
3 papers · 1 filter
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…
Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations
Youngjoon Hong, Bongsuk Kwon, Byung-Jun Yoon
We consider the optimal experimental design problem for an uncertain Kuramoto model, which consists of N interacting oscillators described by coupled ordinary differential equation…