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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...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Royal Society of Chemistry
2019
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| 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 |