Приближенное решения 2D уравнения Навье-Стокса методом Фурье-нейрооператора; Перспективы развития фундаментальных наук; Т. 3 : Математика
| Parent link: | Перспективы развития фундаментальных наук=Prospects of Fundamental Sciences Development: сборник научных трудов XХII Международной конференции студентов, аспирантов и молодых ученых, г. Томск, 22-25 апреля 2025/ Национальный исследовательский Томский политехнический университет ; под ред. И. А. Курзиной [и др.].— .— Томск: Изд-во ТПУ Т. 3 : Математика.— 2025.— С. 72-75 |
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| Gaia: | Заглавие с экрана The Navier-Stokes equation, along with other equations of hydrogas dynamics, is nontrivial and generally has no analytical solution. Some simplifications make it possible to obtain an analytical solution to this equation, but in practice they often resort to its numerical solution. There are a number of classical methods for this.: finite difference method; finite volume method; finite element method. Classical approaches to solving the Navier-Stokes equation lead to lengthy calculations with the slightest change in equation parameters, initial or boundary conditions. Modern approaches based on neural network models, such as convolutional neural networks, make it possible to optimize the solution of such equations, but they strongly depend on data discretization. The neural Fourier operator avoids this dependence by training the model on data not in the time domain, but in the spectral domain. This approach makes it possible to significantly reduce the time needed to solve the equations of hydrogas dynamics, while maintaining the flexibility of the model Текстовый файл |
| Hizkuntza: | errusiera |
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2025
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| Sarrera elektronikoa: | http://earchive.tpu.ru/handle/11683/133109 |
| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=682738 |
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| 200 | 1 | |a Приближенное решения 2D уравнения Навье-Стокса методом Фурье-нейрооператора |d Approximate solution of the 2D Navier-Stokes equation using the Fourier Neural Operator method |f А. С. Кандыбо |g науч. рук. Б. С. Мерзликин |z eng | |
| 300 | |a Заглавие с экрана | ||
| 320 | |a Список литературы: 3 назв | ||
| 330 | |a The Navier-Stokes equation, along with other equations of hydrogas dynamics, is nontrivial and generally has no analytical solution. Some simplifications make it possible to obtain an analytical solution to this equation, but in practice they often resort to its numerical solution. There are a number of classical methods for this.: finite difference method; finite volume method; finite element method. Classical approaches to solving the Navier-Stokes equation lead to lengthy calculations with the slightest change in equation parameters, initial or boundary conditions. Modern approaches based on neural network models, such as convolutional neural networks, make it possible to optimize the solution of such equations, but they strongly depend on data discretization. The neural Fourier operator avoids this dependence by training the model on data not in the time domain, but in the spectral domain. This approach makes it possible to significantly reduce the time needed to solve the equations of hydrogas dynamics, while maintaining the flexibility of the model | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |0 682243 |t Перспективы развития фундаментальных наук |l Prospects of Fundamental Sciences Development |o сборник научных трудов XХII Международной конференции студентов, аспирантов и молодых ученых, г. Томск, 22-25 апреля 2025 |9 682243 |c Томск |n Изд-во ТПУ |f Национальный исследовательский Томский политехнический университет ; под ред. И. А. Курзиной [и др.] | |
| 463 | 1 | |0 682257 |9 682257 |t Т. 3 : Математика |d 2025 |u conference_tpu-2025-C21_V3.pdf |v С. 72-75 |l Vol. 3 : Mathematics | |
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| 610 | 1 | |a Navier-Stokes equation | |
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| 610 | 1 | |a invariance to discretization | |
| 700 | 1 | |a Кандыбо |b А. С. | |
| 702 | 1 | |a Мерзликин |b Б. С. |c математик |c инженер-исследователь, старший преподаватель Томского политехнического университета, кандидат физико-математических наук |f 1987- |g Борис Сергеевич |4 727 | |
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