Operator compression with deep neural networks

This paper studies the compression of partial differential operators using neural networks. We consider a family of operators, parameterized by a potentially high-dimensional space of coefficients that may vary on a large range of scales. Based on the existing methods that compress such a multiscale...

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Detalles Bibliográficos
Autores principales: Kröpfl, Fabian, Maier, Roland, Peterseim, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028012/
https://www.ncbi.nlm.nih.gov/pubmed/35531267
http://dx.doi.org/10.1186/s13662-022-03702-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028012/
https://www.ncbi.nlm.nih.gov/pubmed/35531267
http://dx.doi.org/10.1186/s13662-022-03702-y

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