3 citations · 4 across the 6 of their papers we have counts for
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Data-Free Asymptotics-Informed Operator Networks for Singularly Perturbed PDEs
Jinsil Lee, Youngjoon Hong, Seungchan Ko +1
Recent advances in machine learning (ML) have opened new possibilities for solving partial differential equations (PDEs), yet robust performance in challenging regimes remains limi…
Discontinuous Galerkin finite element operator network for solving non-smooth PDEs
Kapil Chawla, Youngjoon Hong, Jae Yong Lee +1
We introduce Discontinuous Galerkin Finite Element Operator Network (DG--FEONet), a data-free operator learning framework that combines the strengths of the discontinuous Galerkin…
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
Youngjoon Hong, Seungchan Ko, Jaeyong Lee
In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called th…
Finite Element Operator Network for Solving Elliptic-type parametric PDEs
Jae Yong Lee, Seungchan Ko, Youngjoon Hong
Partial differential equations (PDEs) underlie our understanding and prediction of natural phenomena across numerous fields, including physics, engineering, and finance. However, s…