Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data-Reference-Cited by-同舟云学术

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Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data

Published:2020-01-01 Issue:1 Volume:32 Page:015113
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Vaddireddy Harsha¹,Rasheed Adil²^ORCID,Staples Anne E.³^ORCID,San Omer¹^ORCID

Affiliation:

1. School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, USA

2. Department of Engineering Cybernetics, Norwegian University of Science and Technology, N-7465 Trondheim, Norway

3. Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia 24061, USA

Funder

U.S. Department of Energy

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.5136351

Reference125 articles.

1. Machine learning: Trends, perspectives, and prospects

2. The big challenges of big data

3. The perceptron: A probabilistic model for information storage and organization in the brain.

4. Deep learning

5. Enhancing Sparsity by Reweighted ℓ 1 Minimization

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