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Coarse-scale PDEs from fine-scale observations via machine learning

Chaos An Interdisciplinary Journal of Nonlinear Science(2020)10.1063/1.5126869Source: DataRank Database

Coarse-scale PDEs from fine-scale observations via machine learning is a research paper published in Chaos An Interdisciplinary Journal of Nonlinear Science (2020). On theSindex it has a DataRank of 0. It has been cited 71 times.

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Open Access71 citations

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Enrichment:Funding (2 grants)Impact (FWCI 5.46)OA: bronzeTopics (12)IDs (PubMed, PMC, OpenAlex)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Mahdi Kooshkbaghi,Konstantinos Spiliotis,Constantinos I. Siettos,Ioannis G. Kevrekidis,Seungjoon Lee ORCID

Live enrichment

PubMed:32013472 PMC:PMC7043837 OpenAlex:https://openalex.org/W3002310157

National Institutes of Health

Defense Advanced Research Projects Agency

FWCI

5.46

Citation Percentile

1.0%

Citation Trend

2020

2021

2022

2023

2024

2025

2026

bronzeLicense: https://publishing.aip.org/authors/rights-and-permissions

journalhttps://aip.scitation.org/doi/pdf/10.1063/1.5126869

publisherhttps://aip.scitation.org/doi/pdf/10.1063/1.5126869

publisherhttp://aip.scitation.org/doi/am-pdf/10.1063/1.5126869

publisherhttps://pubs.aip.org/aip/cha/article-pdf/doi/10.1063/1.5126869/19791674/013141_1_online.pdf

journalhttps://doi.org/10.1063/1.5126869

Fields of Study

Model Reduction and Neural NetworksMachine Learning in Materials ScienceLattice Boltzmann Simulation Studies

Keywords

ObservableArtificial neural networkGaussian processPartial differential equationLattice Boltzmann methodsRelation (database)Process (computing)Lattice (music)Gaussian

Coarse-scale PDEs from fine-scale observations via machine learning

FAIR Checklist

Authors (5)

Funding (2 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)