Application of deep learning techniques in modelling and observation of the solar photosphere
This work is part of the applications of neural networks in the study and modeling of the phenomena presentin the solar photosphere. The proposed research is based on the network model generative adversaries usingPytorch’s artificial intelligence modules. We aim at training a neural network capable...
| Main Authors: | , , , |
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2022
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| Online Access: | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/15240 |
| _version_ | 1801706357485731840 |
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| author | Morales Suarez, Germain Nicolas Agudelo Ortiz, Juan Esteban Santiago Vargas Dominguez Shelyag, Sergiy |
| author_facet | Morales Suarez, Germain Nicolas Agudelo Ortiz, Juan Esteban Santiago Vargas Dominguez Shelyag, Sergiy |
| author_sort | Morales Suarez, Germain Nicolas |
| collection | OJS |
| description | This work is part of the applications of neural networks in the study and modeling of the phenomena presentin the solar photosphere. The proposed research is based on the network model generative adversaries usingPytorch’s artificial intelligence modules. We aim at training a neural network capable of generating groupsof images of a high similarity with input images, These images correspond to physical magnitudes of thesolar photosphere such as density, field magnetic field, plasma velocity, temperature, among others, obtainedfrom the MURaM simulation code, although the neural network can be trained to generate images of anyphysical magnitude. The work is focused on the generation of magnetic field images in the solar photosphere.Results of the neural network training process are presented, as well as the comparison between the trainingand generated images, and the challenges to use these tools in the study of the solar photosphere. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-15240 |
| institution | Revista Ciencia en Desarrollo |
| language | spa |
| publishDate | 2022 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-152402023-03-06T15:47:01Z Application of deep learning techniques in modelling and observation of the solar photosphere Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar Morales Suarez, Germain Nicolas Agudelo Ortiz, Juan Esteban Santiago Vargas Dominguez Shelyag, Sergiy GAN, DCGAN, Pytorch, fotósfera. GAN, DCGAN, Pytorch, photosphere. This work is part of the applications of neural networks in the study and modeling of the phenomena presentin the solar photosphere. The proposed research is based on the network model generative adversaries usingPytorch’s artificial intelligence modules. We aim at training a neural network capable of generating groupsof images of a high similarity with input images, These images correspond to physical magnitudes of thesolar photosphere such as density, field magnetic field, plasma velocity, temperature, among others, obtainedfrom the MURaM simulation code, although the neural network can be trained to generate images of anyphysical magnitude. The work is focused on the generation of magnetic field images in the solar photosphere.Results of the neural network training process are presented, as well as the comparison between the trainingand generated images, and the challenges to use these tools in the study of the solar photosphere. Este trabajo se enmarca en las aplicaciones de las redes neuronales en el estudio y modelamiento delos fenómenos presentes en la fotósfera solar. La investigación propuesta se basa en el modelo de redesadversarias generativas haciendo uso de las módulos de inteligencia artificial de Pytorch. Se busca entrenaruna red neuronal capaz de generar grupos de imágenes de una alta similitud con imágenes de entrenamiento,dichas imágenes corresponden a magnitudes físicas de la fotósfera solar tales como densidad, campomagnético, velocidad del plasma, temperatura, entre otras, obtenidas del código de simulación MURaM,aunque la red neuronal puede entrenarse para generar imágenes de cualquier magnitud física. El trabajo seenfoca en la generación de imágenes de campo magnético en la fotósfera solar. Se presentan los resultadosde entrenamiento de la red neuronal, la comparativa entre las imágenes de entrenamiento y las imágenesgeneradas, y se proponen los retos para usar estas herramientas en el estudio de la fotósfera solar. Universidad Pedagógica y Tecnológica de Colombia 2022-12-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/15240 10.19053/01217488.v1.n2E.2022.15240 Ciencia En Desarrollo; Vol. 1 No. 2E (2022): Núm. 2E (2022): Número Especial: VII Congreso de Astronomía y Astrofísica 2022; 11-17 Ciencia en Desarrollo; Vol. 1 Núm. 2E (2022): Núm. 2E (2022): Número Especial: VII Congreso de Astronomía y Astrofísica 2022; 11-17 2462-7658 0121-7488 spa https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/15240/12659 |
| spellingShingle | GAN, DCGAN, Pytorch, fotósfera. GAN, DCGAN, Pytorch, photosphere. Morales Suarez, Germain Nicolas Agudelo Ortiz, Juan Esteban Santiago Vargas Dominguez Shelyag, Sergiy Application of deep learning techniques in modelling and observation of the solar photosphere |
| title | Application of deep learning techniques in modelling and observation of the solar photosphere |
| title_alt | Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| title_full | Application of deep learning techniques in modelling and observation of the solar photosphere |
| title_fullStr | Application of deep learning techniques in modelling and observation of the solar photosphere |
| title_full_unstemmed | Application of deep learning techniques in modelling and observation of the solar photosphere |
| title_short | Application of deep learning techniques in modelling and observation of the solar photosphere |
| title_sort | application of deep learning techniques in modelling and observation of the solar photosphere |
| topic | GAN, DCGAN, Pytorch, fotósfera. GAN, DCGAN, Pytorch, photosphere. |
| topic_facet | GAN, DCGAN, Pytorch, fotósfera. GAN, DCGAN, Pytorch, photosphere. |
| url | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/15240 |
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