3 papers
cs.LG2025
A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations
Junho Choi, Teng-Yuan Chang, Namjung Kim +1
Ensemble simulations of high-dimensional flow models (e.g., Navier Stokes type PDEs) are computationally prohibitive for real time applications. Neural operators enable fast infere…
cs.LG2023
Spectral operator learning for parametric PDEs without data reliance
Junho Choi, Taehyun Yun, Namjung Kim +1
In this paper, we introduce the Spectral Coefficient Learning via Operator Network (SCLON), a novel operator learning-based approach for solving parametric partial differential equ…
cs.LG2022
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations
Junho Choi, Namjung Kim, Youngjoon Hong
Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research fiel…