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Extensive deep neural networks for transferring small scale learning to large scale systems

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with [Image: see text] scaling. We use a form of domain decomposition for training and inferenc...

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Detalles Bibliográficos
Autores principales: Mills, Kyle, Ryczko, Kevin, Luchak, Iryna, Domurad, Adam, Beeler, Chris, Tamblyn, Isaac
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royal Society of Chemistry 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460955/
https://www.ncbi.nlm.nih.gov/pubmed/31015950
http://dx.doi.org/10.1039/c8sc04578j

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