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Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations
Author(s):
Enrico Schiassi
,
Roberto Furfaro
,
Carl Leake
,
Mario De Florio
,
Hunter Johnston
,
Daniele Mortari
Publication date
Created:
October 2021
Publication date
(Print):
October 2021
Journal:
Neurocomputing
Publisher:
Elsevier BV
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Numerical Algebra, Matrix Theory, Differential-Algebraic Equations, and Control Theory
Most cited references
54
Record
: found
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Extreme learning machine: Theory and applications
Guang-Bin Huang
,
Qin-Yu Zhu
,
Chee-Kheong Siew
(2007)
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Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
M. Raissi
,
P Perdikaris
,
G.E. Karniadakis
(2019)
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Approximation by superpositions of a sigmoidal function
G. Cybenko
(1989)
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Author and article information
Journal
Title:
Neurocomputing
Abbreviated Title:
Neurocomputing
Publisher:
Elsevier BV
ISSN (Print):
09252312
Publication date Created:
October 2021
Publication date (Print):
October 2021
Volume
: 457
Pages
: 334-356
Article
DOI:
10.1016/j.neucom.2021.06.015
SO-VID:
bb477b4f-52ad-40a4-84f2-28fe66565e49
Copyright ©
© 2021
License:
https://www.elsevier.com/tdm/userlicense/1.0/
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