A neural network reduced order model for the elasticity problem
The work investigates two different approaches to solving the elasticity problem for material with inclusions. We apply the finite element method for the classic approach. Meanwhile, we also apply a neural network approach to construct a solver based on the solutions obtained firstly. This approach...
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| creator | Grigorev, Aleksei Grigorev, Aleksandr Sivtsev, Petr Stepanov, Sergei |
| description | The work investigates two different approaches to solving the elasticity problem for material with inclusions. We apply the finite element method for the classic approach. Meanwhile, we also apply a neural network approach to construct a solver based on the solutions obtained firstly. This approach consists in training convolutional neural networks on a family of solutions represented as images. Based on the results of applying the two approaches, the effectiveness and applicability of the methods for solving the problem are demonstrated. |
| doi_str_mv | 10.1063/5.0107117 |
| format | Conference Proceeding |
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We apply the finite element method for the classic approach. Meanwhile, we also apply a neural network approach to construct a solver based on the solutions obtained firstly. This approach consists in training convolutional neural networks on a family of solutions represented as images. Based on the results of applying the two approaches, the effectiveness and applicability of the methods for solving the problem are demonstrated.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0107117</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Artificial neural networks ; Elasticity ; Finite element method ; Inclusions ; Reduced order models</subject><ispartof>AIP conference proceedings, 2022, Vol.2528 (1)</ispartof><rights>Author(s)</rights><rights>2022 Author(s). 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Based on the results of applying the two approaches, the effectiveness and applicability of the methods for solving the problem are demonstrated.</description><subject>Artificial neural networks</subject><subject>Elasticity</subject><subject>Finite element method</subject><subject>Inclusions</subject><subject>Reduced order models</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kE1LAzEQhoMoWKsH_0HAm7B1Jh-bzUlK8QsKXhS8hWySxa3bpmZ3lf57Iy148_Qe5pl5hpeQS4QZQslv5AwQFKI6IhOUEgtVYnlMJgBaFEzwt1Ny1vcrAKaVqibkdk43YUy2yzF8x_RBU_CjC57G5EOi6-hDR5uY6PAeaOhsP7SuHXZ0m2LdhfU5OWls14eLQ07J6_3dy-KxWD4_PC3my8JlqSoQUWjPQ5CKWcbqygsOrPYMJIPgvMPKMeurxjspKifyiGsNtqx16bRQfEqu9nez93MM_WBWcUybrDRMIQCiFDxT13uqzz_aoY0bs03t2qad-YrJSHMox2x98x-MYH7b_FvgP7KoZTc</recordid><startdate>20220906</startdate><enddate>20220906</enddate><creator>Grigorev, Aleksei</creator><creator>Grigorev, Aleksandr</creator><creator>Sivtsev, Petr</creator><creator>Stepanov, Sergei</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20220906</creationdate><title>A neural network reduced order model for the elasticity problem</title><author>Grigorev, Aleksei ; Grigorev, Aleksandr ; Sivtsev, Petr ; Stepanov, Sergei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2437-11149d3ee572a22b8d4302bd20520ecdc18c2ad8fdc548c42bd3990a6b96c9473</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial neural networks</topic><topic>Elasticity</topic><topic>Finite element method</topic><topic>Inclusions</topic><topic>Reduced order models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Grigorev, Aleksei</creatorcontrib><creatorcontrib>Grigorev, Aleksandr</creatorcontrib><creatorcontrib>Sivtsev, Petr</creatorcontrib><creatorcontrib>Stepanov, Sergei</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Grigorev, Aleksei</au><au>Grigorev, Aleksandr</au><au>Sivtsev, Petr</au><au>Stepanov, Sergei</au><au>Sharin, Egor P.</au><au>Grigor’ev, Yuri M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A neural network reduced order model for the elasticity problem</atitle><btitle>AIP conference proceedings</btitle><date>2022-09-06</date><risdate>2022</risdate><volume>2528</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>The work investigates two different approaches to solving the elasticity problem for material with inclusions. 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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2022, Vol.2528 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0107117 |
| source | AIP Journals Complete |
| subjects | Artificial neural networks Elasticity Finite element method Inclusions Reduced order models |
| title | A neural network reduced order model for the elasticity problem |
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