1 citations · 1 across the 3 of their papers we have counts for
3 papers
math.AP2024
On the existence of strong solutions for unsteady motions of incompressible chemically reacting generalized Newtonian fluids
Kyueon Choi, Kyungkeun Kang, Seungchan Ko
We consider a system of nonlinear partial differential equations modeling the unsteady motion of an incompressible generalized Newtonian fluid with chemical reactions. The system c…
math.NA2024
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
Seungchan Ko, Sang Hyeon Park
Physics-informed neural networks (PINNs) have recently emerged as a promising way to compute the solutions of partial differential equations (PDEs) using deep neural networks. Howe…
math.NA2024★ 1 cited
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
Youngjoon Hong, Seungchan Ko, Jaeyong Lee
In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called th…