4 citations · 6 across the 10 of their papers we have counts for
5 papers · 1 filter
Singular layer PINN methods for Burgers' equation
Teng-Yuan Chang, Gung-Min Gie, Youngjoon Hong +1
In this article, we present a new learning method called sl-PINN to tackle the one-dimensional viscous Burgers problem at a small viscosity, which results in a singular interior la…
Semi-analytic PINN methods for boundary layer problems in a rectangular domain
Gung-Min Gie, Youngjoon Hong, Chang-Yeol Jung +1
Singularly perturbed boundary value problems pose a significant challenge for their numerical approximations because of the presence of sharp boundary layers. These sharp boundary…
Singular Layer Physics-Informed Neural Network Method for Convection-Dominated Boundary Layer Problems in Two Dimensions
Gung-Min Gie, Youngjoon Hong, Chang-Yeol Jung +1
This research explores neural network-based numerical approximation of two-dimensional convection-dominated singularly perturbed problems on square, circular, and elliptic domains.…
Singular layer Physics Informed Neural Network method for Plane Parallel Flows
Teng-Yuan Chang, Gung-Min Gie, Youngjoon Hong +1
We construct in this article the semi-analytic Physics Informed Neural Networks (PINNs), called {\em singular layer PINNs} (or {\em sl-PINNs}), that are suitable to predict the sti…
Semi-analytic PINN methods for singularly perturbed boundary value problems
Gung-Min Gie, Youngjoon Hong, Chang-Yeol Jung
We propose a new semi-analytic physics informed neural network (PINN) to solve singularly perturbed boundary value problems. The PINN is a scientific machine learning framework tha…