Neural operators: Difference between revisions

Revision as of 07:05, 20 November 2023

Revision as of 07:05, 20 November 2023

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{{AFC comment|1=Seems notable enough in the sense that it is a concept that have seen widespread adapation, but a reviewer with knowledge on this topic is needed. [[User:Ca|Ca]] ^{<i>[[User talk:Ca|talk to me!]]</i>} 13:50, 19 November 2023 (UTC)}}

{{AFC comment|1=Not really enough independent, significant coverage to show why this is notable enough for Wikipedia [[User:WikiOriginal-9|WikiOriginal-9]] ([[User talk:WikiOriginal-9|talk]]) 02:17, 7 November 2023 (UTC)}}

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{{Short description|Machine learning framework}}

{{Draft topics|computing}}

{{AfC topic|stem}}

'''Neural operators''' are a class of [[deep learning]] architectures designed to learn maps between infinite-dimensional [[Function space|function spaces]]. Neural operators represent an extension of traditional [[Artificial neural network|artificial neural networks]], marking a departure from the typical focus on learning mappings between finite-dimensional Euclidean spaces or finite sets. Neural operators directly learn [[Operator (mathematics)|operators]] between function spaces; they can receive input functions, and the output function can be evaluated at any discretization.<ref name="NO journal">{{cite journal |last1=Kovachki |first1=Nikola |last2=Li |first2=Zongyi |last3=Liu |first3=Burigede |last4=Azizzadenesheli |first4=Kamyar |last5=Bhattacharya |first5=Kaushik |last6=Stuart |first6=Andrew |last7=Anandkumar |first7=Anima |title=Neural operator: Learning maps between function spaces |journal=Journal of Machine Learning Research |date=2021 |volume=24 |page=1-97 |arxiv=2108.08481 |url=https://www.jmlr.org/papers/volume24/21-1524/21-1524.pdf}}</ref>

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== References ==

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