Simulation of the nickel superalloys solvus temperature by the deep learning artificial neural network with differential layer
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Simulation of the nickel superalloys solvus temperature by the deep learning artificial neural network with differential layer
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| Автор |
Tarasov, D.
Tyagunov, A. Milder, O. |
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| Тематика |
ARTIFICIAL NEURAL NETWORK
FRAMEWORK NICKEL SUPERALLOYS SIMULATION SOLVUS TEMPERATURE |
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| Описание |
Simulating the properties of complex alloys is an extremely challenging scientific task. The model should take into account a large number of uncorrelated factors, for many of which information may be absent or vague. The individual contribution of one or another chemical element out of a dozen possible ligants cannot be determined by traditional methods, and there are no general analytical models describing the effect of elements on the characteristics of alloys. Artificial neural networks are one of the few statistical simulation tools that may account many implicit correlations and establish correspondences that cannot be identified by other, more familiar mathematical methods. However, networks require complex tuning to achieve high performance. Data engineering and data preprocessing also makes a great contribution. This paper focuses on combining deep network configuration selection based on physics and input engineering to simulate the solvus temperature of nickel superalloys. © 2022 Author(s).
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| Дата |
2024-04-22T15:52:56Z
2024-04-22T15:52:56Z 2022 |
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| Тип |
Conference paper
Conference object (info:eu-repo/semantics/conferenceObject) Published version (info:eu-repo/semantics/publishedVersion) |
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| Идентификатор |
Tarasov, D, Tyagunov, A & Milder, O 2022, Simulation of the nickel superalloys solvus temperature by the deep learning artificial neural network with differential layer. в T Simos, T Simos, T Simos, C Tsitouras, Z Kalogiratou & T Monovasilis (ред.), International Conference of Computational Methods in Sciences and Engineering, ICCMSE 2021., 130008, AIP Conference Proceedings, Том. 2611, American Institute of Physics Inc., International Conference of Computational Methods in Sciences and Engineering 2021, ICCMSE 2021, Heraklion, Греция, 04/09/2021. https://doi.org/10.1063/5.0119488
Tarasov, D., Tyagunov, A., & Milder, O. (2022). Simulation of the nickel superalloys solvus temperature by the deep learning artificial neural network with differential layer. в T. Simos, T. Simos, T. Simos, C. Tsitouras, Z. Kalogiratou, & T. Monovasilis (Ред.), International Conference of Computational Methods in Sciences and Engineering, ICCMSE 2021 [130008] (AIP Conference Proceedings; Том 2611). American Institute of Physics Inc.. https://doi.org/10.1063/5.0119488 978-073544247-4 0094-243X Final All Open Access; Bronze Open Access https://aip.scitation.org/doi/pdf/10.1063/5.0119488 https://aip.scitation.org/doi/pdf/10.1063/5.0119488 http://elar.urfu.ru/handle/10995/132371 10.1063/5.0119488 85143158745 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
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| Формат |
application/pdf
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| Издатель |
American Institute of Physics Inc.
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| Источник |
AIP Conference Proceedings
AIP Conference Proceedings |
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