Seungchan Ko

According to our database, Seungchan Ko authored at least 16 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Sobolev Approximation of Deep ReLU Networks in Log-Barron Space.

Changhoon Song, Seungchan Ko, Youngjoon Hong

CoRR, January, 2026

Sparse FEONet: A Low-Cost, Memory-Efficient Operator Network via Finite-Element Local Sparsity for Parametric PDEs.

Seungchan Ko, Jiyeon Kim, Dongwook Shin

CoRR, January, 2026

Task-aware evolution in physics-informed neural networks: Application to Saint-Venant torsion problems.

Su Yeong Jo, Sanghyeon Park, Jeesuk Shin, Jongcheon Park, Hosung Kim, Seungchan Ko, Sangseung Lee, Joongoo Jeon

Eng. Appl. Artif. Intell., 2026

2025

Data-free Asymptotics-Informed Operator Networks for Singularly Perturbed PDEs.

Jinsil Lee, Youngjoon Hong, Seungchan Ko, Jae Yong Lee

CoRR, December, 2025

Locking-Free Training of Physics-Informed Neural Network for Solving Nearly Incompressible Elasticity Equations.

Josef Dick, Seungchan Ko, Kassem Mustapha, Sanghyeon Park

CoRR, May, 2025

Engineering application of physics-informed neural networks for Saint-Venant torsion.

Su Yeong Jo, Sanghyeon Park, Seungchan Ko, Jongcheon Park, Hosung Kim, Sangseung Lee, Joongoo Jeon

CoRR, May, 2025

Error analysis for a fully-discrete finite element approximation of the unsteady p(·,·)-Stokes equations.

Luigi C. Berselli, Alex Kaltenbach, Seungchan Ko

CoRR, January, 2025

Finite Element Operator Network for Solving Elliptic-Type Parametric PDEs.

Jae Yong Lee, Seungchan Ko, Youngjoon Hong

SIAM J. Sci. Comput., 2025

VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior.

Seungchan Ko, Sanghyeon Park

J. Comput. Phys., 2025

2024

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

CoRR, 2024

Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs.

Youngjoon Hong, Seungchan Ko, Jae Yong Lee

CoRR, 2024

2023

A novel approach for wafer defect pattern classification based on topological data analysis.

Seungchan Ko, Dowan Koo

Expert Syst. Appl., November, 2023

Finite Element Operator Network for Solving Parametric PDEs.

Jae Yong Lee, Seungchan Ko, Youngjoon Hong

CoRR, 2023

2022

Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs.

Seungchan Ko, Seok-Bae Yun, Youngjoon Hong

CoRR, 2022

Quasi-Monte Carlo finite element approximation of the Navier-Stokes equations with initial data modeled by log-normal random fields.

Seungchan Ko, Guanglian Li, Yi Yu

CoRR, 2022

2019

Finite element approximation of steady flows of generalized Newtonian fluids with concentration-dependent power-law index.

Seungchan Ko, Endre Süli

Math. Comput., 2019