Seungchan Ko
Orcid: 0000-0002-6199-442X
According to our database1,
Seungchan Ko
authored at least 12 papers
between 2019 and 2025.
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Bibliography
2025
Locking-Free Training of Physics-Informed Neural Network for Solving Nearly Incompressible Elasticity Equations.
CoRR, May, 2025
Engineering application of physics-informed neural networks for Saint-Venant torsion.
CoRR, May, 2025
Error analysis for a fully-discrete finite element approximation of the unsteady p(·,·)-Stokes equations.
CoRR, January, 2025
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.
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.
CoRR, 2024
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs.
CoRR, 2024
2023
A novel approach for wafer defect pattern classification based on topological data analysis.
Expert Syst. Appl., November, 2023
2022
Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs.
CoRR, 2022
Quasi-Monte Carlo finite element approximation of the Navier-Stokes equations with initial data modeled by log-normal random fields.
CoRR, 2022
2019
Finite element approximation of steady flows of generalized Newtonian fluids with concentration-dependent power-law index.
Math. Comput., 2019