Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows
We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The AE model is assessed with four canonical flui...
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| Veröffentlicht in: | SN computer science 2021-11, Vol.2 (6), p.467, Article 467 |
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| creator | Fukami, Kai Hasegawa, Kazuto Nakamura, Taichi Morimoto, Masaki Fukagata, Koji |
| description | We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The AE model is assessed with four canonical fluid flows, namely: (1) two-dimensional cylinder wake, (2) its transient process, (3) NOAA sea surface temperature, and (4) a cross-sectional field of turbulent channel flow, in terms of a number of latent modes, the choice of nonlinear activation functions, and the number of weights contained in the AE model. We find that the AE models are sensitive to the choice of the aforementioned parameters depending on the target flows. Finally, we foresee the extensional applications and perspectives of machine learning based order reduction for numerical and experimental studies in the fluid dynamics community. |
| doi_str_mv | 10.1007/s42979-021-00867-3 |
| format | Article |
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As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The AE model is assessed with four canonical fluid flows, namely: (1) two-dimensional cylinder wake, (2) its transient process, (3) NOAA sea surface temperature, and (4) a cross-sectional field of turbulent channel flow, in terms of a number of latent modes, the choice of nonlinear activation functions, and the number of weights contained in the AE model. We find that the AE models are sensitive to the choice of the aforementioned parameters depending on the target flows. Finally, we foresee the extensional applications and perspectives of machine learning based order reduction for numerical and experimental studies in the fluid dynamics community.</description><identifier>ISSN: 2662-995X</identifier><identifier>EISSN: 2661-8907</identifier><identifier>DOI: 10.1007/s42979-021-00867-3</identifier><language>eng</language><publisher>Singapore: Springer Singapore</publisher><subject>Artificial neural networks ; Channel flow ; Computer Imaging ; Computer Science ; Computer Systems Organization and Communication Networks ; Data Structures and Information Theory ; Dynamical systems ; Fluid dynamics ; Fluid flow ; Information Systems and Communication Service ; Laminar flow ; Machine learning ; Methods ; Model reduction ; Multilayer perceptrons ; Multilayers ; Neural networks ; Original Research ; Parameter sensitivity ; Pattern Recognition and Graphics ; Reynolds number ; Sea surface temperature ; Software Engineering/Programming and Operating Systems ; Turbulent flow ; Two dimensional flow ; Vision</subject><ispartof>SN computer science, 2021-11, Vol.2 (6), p.467, Article 467</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. 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Hasegawa, Kazuto ; Nakamura, Taichi ; Morimoto, Masaki ; Fukagata, Koji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2783-8f6d9fb95887617fc3aaae53d6fcb57131804c3e0079d21f045a4578aa3b3b283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Channel flow</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>Data Structures and Information Theory</topic><topic>Dynamical systems</topic><topic>Fluid dynamics</topic><topic>Fluid flow</topic><topic>Information Systems and Communication Service</topic><topic>Laminar flow</topic><topic>Machine learning</topic><topic>Methods</topic><topic>Model reduction</topic><topic>Multilayer perceptrons</topic><topic>Multilayers</topic><topic>Neural networks</topic><topic>Original Research</topic><topic>Parameter sensitivity</topic><topic>Pattern Recognition and Graphics</topic><topic>Reynolds number</topic><topic>Sea surface temperature</topic><topic>Software Engineering/Programming and Operating Systems</topic><topic>Turbulent flow</topic><topic>Two dimensional flow</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fukami, Kai</creatorcontrib><creatorcontrib>Hasegawa, Kazuto</creatorcontrib><creatorcontrib>Nakamura, Taichi</creatorcontrib><creatorcontrib>Morimoto, Masaki</creatorcontrib><creatorcontrib>Fukagata, Koji</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>SN computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fukami, Kai</au><au>Hasegawa, Kazuto</au><au>Nakamura, Taichi</au><au>Morimoto, Masaki</au><au>Fukagata, Koji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows</atitle><jtitle>SN computer science</jtitle><stitle>SN COMPUT. 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| subjects | Artificial neural networks Channel flow Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Dynamical systems Fluid dynamics Fluid flow Information Systems and Communication Service Laminar flow Machine learning Methods Model reduction Multilayer perceptrons Multilayers Neural networks Original Research Parameter sensitivity Pattern Recognition and Graphics Reynolds number Sea surface temperature Software Engineering/Programming and Operating Systems Turbulent flow Two dimensional flow Vision |
| title | Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows |
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