Zobrazeno 1 - 10
of 42
pro vyhledávání: '"YOUNGJOON HONG"'
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-10 (2024)
Abstract Recent advancements in artificial intelligence (AI)-based design strategies for metamaterials have revolutionized the creation of customizable architectures spanning nano- to macro-scale dimensions. However, their increasing complexity poses
Externí odkaz:
https://doaj.org/article/bd4e02a3e6234dd1ba8a83191f53c5f6
Publikováno v:
IEEE Access, Vol 11, Pp 23433-23446 (2023)
In recent years, machine learning methods have been used to solve partial differential equations (PDEs) and dynamical systems, leading to the development of a new research field called scientific machine learning, which combines techniques such as de
Externí odkaz:
https://doaj.org/article/61fca703012645599b3e4a4e4112a771
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Accurate epidemiological models are necessary for governments, organizations, and individuals to respond appropriately to the ongoing novel coronavirus pandemic. One informative metric epidemiological models provide is the basic reproduction
Externí odkaz:
https://doaj.org/article/a57b058191704a5bb9a665f38fc0c41e
Autor:
Yuri Kim, Myung Ho Jeong, Minjeong An, Kyunghoon Cho, Youngjoon Hong, Juhan Kim, Youngkeun Ahn
Publikováno v:
PLoS ONE, Vol 18, Iss 2, p e0278993 (2023)
Background and objectivesTo compare the long-term clinical outcomes of dual antiplatelet therapy (DAPT) with clopidogrel and DAPT with ticagrelor or prasugrel in patients with acute myocardial infarction (AMI) who underwent coronary intervention.Meth
Externí odkaz:
https://doaj.org/article/d044d907bcbe486aaeab8322fd305580
Publikováno v:
IEEE Access, Vol 9, Pp 53804-53810 (2021)
We consider the optimal experimental design (OED) problem for an uncertain system described by coupled ordinary differential equations (ODEs), whose parameters are not completely known. The primary objective of this work is to develop a general exper
Externí odkaz:
https://doaj.org/article/5e1c7825f8f94613914eb174ad6eea3d
Publikováno v:
IEEE Access, Vol 9, Pp 146938-146947 (2021)
Modern neural networks are known to be vulnerable to adversarial attacks in various domains. Although most attack methods usually densely change the input values, recent works have shown that deep neural networks (DNNs) are also vulnerable to sparse
Externí odkaz:
https://doaj.org/article/7b93adf29cfb4975b70c64124a8cca42
DEEP NEURAL NETWORK FOR SOLVING DIFFERENTIAL EQUATIONS MOTIVATED BY LEGENDRE-GALERKIN APPROXIMATION.
Autor:
CHUDOMELKA, BRYCE1 brycechudomelka@gmail.com, YOUNGJOON HONG2 hongyj@kaist.ac.kr, MORGAN, JOHN1 jmorgan4844@sdsu.edu, HYUNWOO KIM3 hyunwoojkim@korea.ac.kr, JINYOUNG PARK3 lpmn678@korea.ac.kr
Publikováno v:
International Journal of Numerical Analysis & Modeling. 2024, Vol. 21 Issue 5, p652-673. 22p.
Publikováno v:
ACS Materials Letters. 5:730-737
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35105997826157600098096f6b1ee53c
https://doi.org/10.1021/acsmaterialslett.2c01096
https://doi.org/10.1021/acsmaterialslett.2c01096
Autor:
Youngjoon Hong, David P. Nicholls
Publikováno v:
Optics express. 30(13)
A deep learning aided optimization algorithm for the design of flat thin-film multilayer optical systems is developed. The authors introduce a deep generative neural network, based on a variational autoencoder, to perform the optimization of photonic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdc334073ee664266a91c04cffb811b8
https://pubmed.ncbi.nlm.nih.gov/36224980
https://pubmed.ncbi.nlm.nih.gov/36224980
Publikováno v:
Applicable Analysis. 102:288-314
Viscous Burgers' equations with a small viscosity are considered and convergence of vanishing viscosity limit problem is investigated. We examine interior layers of a solution to viscous Burgers' e...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c4f85573e0f7d54c53ed881d5886b1c
https://doi.org/10.1080/00036811.2021.1951714
https://doi.org/10.1080/00036811.2021.1951714