Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations
The article is devoted to the current task of selecting pre-training parameters for the synthesis of surrogate models, which is a key factor in creating high-performance models of complex technological objects. During the study, the authors conduct a systematic analysis of various parameters and the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/48/e3sconf_apecvi2023_01006.pdf |
| _version_ | 1797740303873998848 |
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| author | Kilin Grigory Kavalerov Boris Suslov Artem Tyatenkov Ilya |
| author_facet | Kilin Grigory Kavalerov Boris Suslov Artem Tyatenkov Ilya |
| author_sort | Kilin Grigory |
| collection | DOAJ |
| description | The article is devoted to the current task of selecting pre-training parameters for the synthesis of surrogate models, which is a key factor in creating high-performance models of complex technological objects. During the study, the authors conduct a systematic analysis of various parameters and their interactions, including determining the optimal number of training iterations, the number of trainable layers, and the number of neurons in these layers. Thanks to this approach, the results of the presented study can significantly improve the accuracy and efficiency of surrogate models, which in turn leads to simplification and acceleration of the process of their development and application in various fields of science and engineering. |
| first_indexed | 2024-03-12T14:10:31Z |
| format | Article |
| id | doaj.art-40a73fcc18994f4bb82f716644314e31 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T14:10:31Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-40a73fcc18994f4bb82f716644314e312023-08-21T09:01:37ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014110100610.1051/e3sconf/202341101006e3sconf_apecvi2023_01006Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stationsKilin Grigory0Kavalerov Boris1Suslov Artem2Tyatenkov Ilya3Perm National Research Polytechnic UniversityPerm National Research Polytechnic UniversityPerm National Research Polytechnic UniversityPerm National Research Polytechnic UniversityThe article is devoted to the current task of selecting pre-training parameters for the synthesis of surrogate models, which is a key factor in creating high-performance models of complex technological objects. During the study, the authors conduct a systematic analysis of various parameters and their interactions, including determining the optimal number of training iterations, the number of trainable layers, and the number of neurons in these layers. Thanks to this approach, the results of the presented study can significantly improve the accuracy and efficiency of surrogate models, which in turn leads to simplification and acceleration of the process of their development and application in various fields of science and engineering.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/48/e3sconf_apecvi2023_01006.pdf |
| spellingShingle | Kilin Grigory Kavalerov Boris Suslov Artem Tyatenkov Ilya Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations E3S Web of Conferences |
| title | Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations |
| title_full | Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations |
| title_fullStr | Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations |
| title_full_unstemmed | Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations |
| title_short | Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations |
| title_sort | selection of pre training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/48/e3sconf_apecvi2023_01006.pdf |
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