Cargando…

Learning aerodynamics with neural network

We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two orders of less parameters. We further investigate...

Descripción completa

Detalles Bibliográficos
Autores principales: Peng, Wenhui, Zhang, Yao, Laurendeau, Eric, Desmarais, Michel C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043210/
https://www.ncbi.nlm.nih.gov/pubmed/35473951
http://dx.doi.org/10.1038/s41598-022-10737-4
_version_ 1784694824079720448
author Peng, Wenhui
Zhang, Yao
Laurendeau, Eric
Desmarais, Michel C.
author_facet Peng, Wenhui
Zhang, Yao
Laurendeau, Eric
Desmarais, Michel C.
author_sort Peng, Wenhui
collection PubMed
description We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two orders of less parameters. We further investigate and explain how the ESCNN succeeds in making accurate predictions with standard convolution layers. We discover that the ESCNN has the ability to extract physical patterns that emerge from aerodynamics, and such patterns are clearly reflected within a layer of the network. We show that the ESCNN is capable of learning the physical laws and equation of aerodynamics from simulation data.
format Online
Article
Text
id pubmed-9043210
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-90432102022-04-28 Learning aerodynamics with neural network Peng, Wenhui Zhang, Yao Laurendeau, Eric Desmarais, Michel C. Sci Rep Article We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two orders of less parameters. We further investigate and explain how the ESCNN succeeds in making accurate predictions with standard convolution layers. We discover that the ESCNN has the ability to extract physical patterns that emerge from aerodynamics, and such patterns are clearly reflected within a layer of the network. We show that the ESCNN is capable of learning the physical laws and equation of aerodynamics from simulation data. Nature Publishing Group UK 2022-04-26 /pmc/articles/PMC9043210/ /pubmed/35473951 http://dx.doi.org/10.1038/s41598-022-10737-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Peng, Wenhui
Zhang, Yao
Laurendeau, Eric
Desmarais, Michel C.
Learning aerodynamics with neural network
title Learning aerodynamics with neural network
title_full Learning aerodynamics with neural network
title_fullStr Learning aerodynamics with neural network
title_full_unstemmed Learning aerodynamics with neural network
title_short Learning aerodynamics with neural network
title_sort learning aerodynamics with neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043210/
https://www.ncbi.nlm.nih.gov/pubmed/35473951
http://dx.doi.org/10.1038/s41598-022-10737-4
work_keys_str_mv AT pengwenhui learningaerodynamicswithneuralnetwork
AT zhangyao learningaerodynamicswithneuralnetwork
AT laurendeaueric learningaerodynamicswithneuralnetwork
AT desmaraismichelc learningaerodynamicswithneuralnetwork