3 papers
cs.LG2025
Engineering application of physics-informed neural networks for Saint-Venant torsion
Su Yeong Jo, Sanghyeon Park, Seungchan Ko +4
The Saint-Venant torsion theory is a classical theory for analyzing the torsional behavior of structural components, and it remains critically important in modern computational des…
math.NA2025
A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
Josef Dick, Seungchan Ko, Quoc Thong Le Gia +2
Due to divergence instability, the accuracy of low-order conforming finite element methods for nearly incompressible elasticity equations deteriorates as the Lamé coefficient $λ\to…
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…