Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Son, Hwijae"'
Autor:
Cho, Sung Woong, Son, Hwijae
Inverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing methods typically rely on large am
Externí odkaz:
http://arxiv.org/abs/2412.03161
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 9, p e19907 (2020)
BackgroundThe COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. Objective
Externí odkaz:
https://doaj.org/article/742fdd8e20aa4e10bf9336433fb8264f
Physics-Informed Neural Networks (PINNs) have become a prominent application of deep learning in scientific computation, as they are powerful approximators of solutions to nonlinear partial differential equations (PDEs). There have been numerous atte
Externí odkaz:
http://arxiv.org/abs/2205.01059
Publikováno v:
J. Comput. Phys. 494 (2023) 112518
Solutions of certain partial differential equations (PDEs) are often represented by the steepest descent curves of corresponding functionals. Minimizing movement scheme was developed in order to study such curves in metric spaces. Especially, Jordan-
Externí odkaz:
http://arxiv.org/abs/2109.14851
Autor:
Hwang, Hyung Ju, Son, Hwijae
In this paper, we propose a novel conservative formulation for solving kinetic equations via neural networks. More precisely, we formulate the learning problem as a constrained optimization problem with constraints that represent the physical conserv
Externí odkaz:
http://arxiv.org/abs/2106.12147
Physics Informed Neural Networks (PINNs) is a promising application of deep learning. The smooth architecture of a fully connected neural network is appropriate for finding the solutions of PDEs; the corresponding loss function can also be intuitivel
Externí odkaz:
http://arxiv.org/abs/2101.08932
This paper focuses on how to approximate traveling wave solutions for various kinds of partial differential equations via artificial neural networks. A traveling wave solution is hard to obtain with traditional numerical methods when the correspondin
Externí odkaz:
http://arxiv.org/abs/2101.08520
Publikováno v:
In Journal of Computational Physics 1 December 2023 494
Publikováno v:
In Neurocomputing 1 September 2023 548
In this paper, we construct approximated solutions of Differential Equations (DEs) using the Deep Neural Network (DNN). Furthermore, we present an architecture that includes the process of finding model parameters through experimental data, the inver
Externí odkaz:
http://arxiv.org/abs/1907.12925