Deep learning for numerical solutions of Hesegawa-Wakatani system.

In this paper, we use high-order splitting method to discretize the scheme of 2D-Hesegawa-Wakatani system ,generate numerical solutions of this system by BOUT++ and use DNN neural network to train a model from row-zonally average electrical density and vorticity to its local flux.

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Bibliographic Details
Main Author: He, Yutian
Other Authors: Nicolas Privault
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175608
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1756082024-05-06T15:37:04Z Deep learning for numerical solutions of Hesegawa-Wakatani system. He, Yutian Nicolas Privault School of Physical and Mathematical Sciences NPRIVAULT@ntu.edu.sg Mathematical Sciences In this paper, we use high-order splitting method to discretize the scheme of 2D-Hesegawa-Wakatani system ,generate numerical solutions of this system by BOUT++ and use DNN neural network to train a model from row-zonally average electrical density and vorticity to its local flux. Bachelor's degree 2024-04-30T08:47:42Z 2024-04-30T08:47:42Z 2024 Final Year Project (FYP) He, Y. (2024). Deep learning for numerical solutions of Hesegawa-Wakatani system.. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175608 https://hdl.handle.net/10356/175608 en MATH/23/017 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Mathematical Sciences
spellingShingle Mathematical Sciences
He, Yutian
Deep learning for numerical solutions of Hesegawa-Wakatani system.
description In this paper, we use high-order splitting method to discretize the scheme of 2D-Hesegawa-Wakatani system ,generate numerical solutions of this system by BOUT++ and use DNN neural network to train a model from row-zonally average electrical density and vorticity to its local flux.
author2 Nicolas Privault
author_facet Nicolas Privault
He, Yutian
format Final Year Project
author He, Yutian
author_sort He, Yutian
title Deep learning for numerical solutions of Hesegawa-Wakatani system.
title_short Deep learning for numerical solutions of Hesegawa-Wakatani system.
title_full Deep learning for numerical solutions of Hesegawa-Wakatani system.
title_fullStr Deep learning for numerical solutions of Hesegawa-Wakatani system.
title_full_unstemmed Deep learning for numerical solutions of Hesegawa-Wakatani system.
title_sort deep learning for numerical solutions of hesegawa-wakatani system.
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175608
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