Machine-learning inference of fluid variables from data using reservoir computing-Reference-Cited by-同舟云学术

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Machine-learning inference of fluid variables from data using reservoir computing

Published:2018-08-31 Issue:2 Volume:98 Page:
ISSN:2470-0045
Container-title:Physical Review E
language:en
Short-container-title:Phys. Rev. E

Author:

Nakai Kengo,Saiki Yoshitaka

Funder

University of Tokyo

Japan Society for the Promotion of Science

Japan Science and Technology Corporation

Kyoto University

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevE.98.023111/fulltext

Reference25 articles.

1. Finding Density Functionals with Machine Learning

2. Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy

3. Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

4. Multilayer feedforward networks are universal approximators

5. Approximation by superpositions of a sigmoidal function

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4. Reservoir-computing based associative memory and itinerancy for complex dynamical attractors;Nature Communications;2024-06-06

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