13 citations · 15 across the 4 of their papers we have counts for
4 papers
Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis
Yesom Park, Changhoon Song, Myungjoo Kang
Physics-informed neural networks (PINNs) have recently emerged as effective methods for solving partial differential equations (PDEs) in various problems. Substantial research focu…
ReSDF: Redistancing Implicit Surfaces using Neural Networks
Yesom Park, Chang hoon Song, Jooyoung Hahn +1
This paper proposes a deep-learning-based method for recovering a signed distance function (SDF) of a given hypersurface represented by an implicit level set function. Using the fl…
Learning PDE Solution Operator for Continuous Modeling of Time-Series
Yesom Park, Jaemoo Choi, Changyeon Yoon +2
Learning underlying dynamics from data is important and challenging in many real-world scenarios. Incorporating differential equations (DEs) to design continuous networks has drawn…
Minimal Width for Universal Property of Deep RNN
Chang hoon Song, Geonho Hwang, Jun ho Lee +1
A recurrent neural network (RNN) is a widely used deep-learning network for dealing with sequential data. Imitating a dynamical system, an infinite-width RNN can approximate any op…