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Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations

Computing in cardiology(2020)10.22489/cinc.2020.275Source: DataRank Database

Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations is a research paper published in Computing in cardiology (2020). On theSindex it has a DataRank of 0. It has been cited 11 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access11 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)Impact (FWCI 8.35)OA: bronzeTopics (24)IDs (PubMed, PMC, OpenAlex)

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 (9)

Zexin Liu ORCID,Jake Bergquist ORCID,Sumientra Rampersad ORCID,Dan White,Jess Tate ORCID

Live enrichment

PubMed:36845870 PMC:PMC9956381 OpenAlex:https://openalex.org/W3118559110

NIBIB NIH HHS

Grant: U24 EB029012

NIGMS NIH HHS

Grant: P41 GM103545

NIGMS NIH HHS

Grant: R24 GM136986

FWCI

8.35

Citation Percentile

1.0%

Citation Trend

2021

2022

2023

2024

bronze

journalhttps://doi.org/10.22489/cinc.2020.275

publisherhttps://doi.org/10.22489/cinc.2020.275

repositoryhttps://pubmed.ncbi.nlm.nih.gov/36845870

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9956381

Fields of Study

Probabilistic and Robust Engineering DesignCardiac electrophysiology and arrhythmiasModel Reduction and Neural Networks

Keywords

Polynomial chaosUncertainty quantificationParameterized complexityRobustness (evolution)Python (programming language)Computer scienceSensitivity (control systems)Uncertainty analysisPropagation of uncertaintyAlgorithmParameter spaceTorsoStochastic processSensitivity analysisMathematical optimizationSimulationMonte Carlo methodMathematicsMachine learningStatisticsEngineering