Formulating turbulence closures using sparse regression with embedded form invariance

S. Beetham; J. Capecelatro

doi:10.1103/physrevfluids.5.084611

Formulating turbulence closures using sparse regression with embedded form invariance

Physical Review Fluids ◽

10.1103/physrevfluids.5.084611 ◽

2020 ◽

Vol 5 (8) ◽

Author(s):

S. Beetham ◽

J. Capecelatro

Keyword(s):

Sparse Regression ◽

Form Invariance ◽

Turbulence Closures

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Sparse Regression Codes

10.1561/9781680835816 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ranmji Venkataramanan ◽

Sekhar Tatikonda ◽

Andrew Barron

Keyword(s):

Sparse Regression

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Sparse regression for large data sets with outliers

European Journal of Operational Research ◽

10.1016/j.ejor.2021.05.049 ◽

2021 ◽

Author(s):

Lea Bottmer ◽

Christophe Croux ◽

Ines Wilms

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Sparse Regression

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Projection neural network for a class of sparse regression problems with cardinality penalty

Neurocomputing ◽

10.1016/j.neucom.2020.12.045 ◽

2021 ◽

Vol 431 ◽

pp. 188-200

Author(s):

Wenjing Li ◽

Wei Bian

Keyword(s):

Neural Network ◽

Sparse Regression ◽

Regression Problems ◽

Projection Neural Network

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Safe Screening for Sparse Regression with the Kullback-Leibler Divergence

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9414183 ◽

2021 ◽

Author(s):

Cassio F. Dantas ◽

Emmanuel Soubies ◽

Cedric Fevotte

Keyword(s):

Sparse Regression ◽

Leibler Divergence

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Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures

Journal of Fluid Mechanics ◽

10.1017/jfm.2021.148 ◽

2021 ◽

Vol 915 ◽

Author(s):

Bernardo P. Brener ◽

Matheus A. Cruz ◽

Roney L. Thompson ◽

Rodrigo P. Anjos

Keyword(s):

Stokes Equations ◽

Data Driven ◽

Navier Stokes ◽

Reynolds Average Navier Stokes ◽

Navier Stokes Equations ◽

Turbulence Closures ◽

Reynolds Average

Abstract

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Eigenspace perturbations for uncertainty estimation of single-point turbulence closures

Physical Review Fluids ◽

10.1103/physrevfluids.2.024605 ◽

2017 ◽

Vol 2 (2) ◽

Cited By ~ 33

Author(s):

Gianluca Iaccarino ◽

Aashwin Ananda Mishra ◽

Saman Ghili

Keyword(s):

Single Point ◽

Uncertainty Estimation ◽

Turbulence Closures

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Reducing Sampling Ratios and Increasing Number of Estimates Improve Bagging in Sparse Regression

2019 53rd Annual Conference on Information Sciences and Systems (CISS) ◽

10.1109/ciss.2019.8692865 ◽

2019 ◽

Cited By ~ 1

Author(s):

Luoluo Liu ◽

Sang Peter Chin ◽

Trac D. Tran

Keyword(s):

Sparse Regression

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Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

PLoS Computational Biology ◽

10.1371/journal.pcbi.1005234 ◽

2016 ◽

Vol 12 (12) ◽

pp. e1005234 ◽

Cited By ~ 6

Author(s):

Anna Klimovskaia ◽

Stefan Ganscha ◽

Manfred Claassen

Keyword(s):

Time Series ◽

Single Cell ◽

Structure Learning ◽

Reaction Networks ◽

Sparse Regression ◽

Stochastic Reaction ◽

Stochastic Reaction Networks

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Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping

Bioinformatics ◽

10.1093/bioinformatics/btt075 ◽

2013 ◽

Vol 29 (8) ◽

pp. 1026-1034 ◽

Cited By ~ 21

Author(s):

Can Yang ◽

Lin Wang ◽

Shuqin Zhang ◽

Hongyu Zhao

Keyword(s):

Genetic Factors ◽

Low Rank ◽

Eqtl Mapping ◽

Sparse Regression ◽

Low Rank Representation

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Three-order form invariance and conserved quantity

Chinese Physics ◽

10.1088/1009-1963/15/8/005 ◽

2006 ◽

Vol 15 (8) ◽

pp. 1672-1677 ◽

Cited By ~ 5

Author(s):

Yang Xue-Hui ◽

Ma Shan-Jun

Keyword(s):

Conserved Quantity ◽

Form Invariance ◽

Order Form

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