2 citations · 2 across the 3 of their papers we have counts for
5 papers
A derivative-free framework for capturing macroscopic behavior of incompressible turbulent flows
Jihun Han, Yoonsang Lee
The derivative-free loss method (DFLM) is a mesh-free neural network approach for solving partial differential equations by simulating stochastic walkers rather than computing deri…
Robust Fourier Neural Networks
Halyun Jeong, Jihun Han
Fourier embedding has shown great promise in removing spectral bias during neural network training. However, it can still suffer from high generalization errors, especially when th…
Stochastic approach for elliptic problems in perforated domains
Jihun Han, Yoonsang Lee
A wide range of applications in science and engineering involve a PDE model in a domain with perforations, such as perforated metals or air filters. Solving such perforated domain…
Learning In-between Imagery Dynamics via Physical Latent Spaces
Jihun Han, Yoonsang Lee, Anne Gelb
We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps. The complex nature of image data and the lack of temporal in…
An analysis of the derivative-free loss method for solving PDEs
Jihun Han, Yoonsang Lee
This study analyzes the derivative-free loss method to solve a certain class of elliptic PDEs and fluid problems using neural networks. The approach leverages the Feynman-Kac formu…