Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar
ilustraciones, diagramas
- Autores:
-
Morales Suarez, Germain Nicolas
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/85995
- Palabra clave:
- 520 - Astronomía y ciencias afines::522 - Técnicas, procedimientos, aparatos, equipos, materiales
DCGAN
Fotósfera
MHD
Deep learning
CNN
Pytorch
Photosphere
Aprendizaje profundo
Fotosfera
astronomía solar
deep learning
photosphere
solar astronomy
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| dc.title.translated.eng.fl_str_mv |
Application of Deep Learning techniques in modeling and observation of the photosphere |
| title |
Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| spellingShingle |
Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar 520 - Astronomía y ciencias afines::522 - Técnicas, procedimientos, aparatos, equipos, materiales DCGAN Fotósfera MHD Deep learning CNN Pytorch Photosphere Aprendizaje profundo Fotosfera astronomía solar deep learning photosphere solar astronomy |
| title_short |
Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| title_full |
Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| title_fullStr |
Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| title_full_unstemmed |
Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| title_sort |
Aplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solar |
| dc.creator.fl_str_mv |
Morales Suarez, Germain Nicolas |
| dc.contributor.advisor.spa.fl_str_mv |
Vargas Domínguez, Santiago Shelyag, Sergiy |
| dc.contributor.author.spa.fl_str_mv |
Morales Suarez, Germain Nicolas |
| dc.subject.ddc.spa.fl_str_mv |
520 - Astronomía y ciencias afines::522 - Técnicas, procedimientos, aparatos, equipos, materiales |
| topic |
520 - Astronomía y ciencias afines::522 - Técnicas, procedimientos, aparatos, equipos, materiales DCGAN Fotósfera MHD Deep learning CNN Pytorch Photosphere Aprendizaje profundo Fotosfera astronomía solar deep learning photosphere solar astronomy |
| dc.subject.proposal.spa.fl_str_mv |
DCGAN Fotósfera MHD Deep learning CNN |
| dc.subject.proposal.eng.fl_str_mv |
Pytorch Photosphere |
| dc.subject.wikidata.spa.fl_str_mv |
Aprendizaje profundo Fotosfera astronomía solar |
| dc.subject.wikidata.eng.fl_str_mv |
deep learning photosphere solar astronomy |
| description |
ilustraciones, diagramas |
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2023 |
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2023 |
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2024-04-29T19:54:42Z |
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2024-04-29T19:54:42Z |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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M. Schüssler F. Cattaneo T. Emonet A. Vögler, S. Shelyag1 and T. Linde. Simulations of magneto- convection in the solar photosphere equations, methods, and results of the muram code. As- tronomy & Astrophysics, II:8–10, 2004. C. M. Bishop. Neural networks for pattern recognition. Astronomy & Astrophysics, II:0–498, 1996. Roger P Briggs. Solar Physics and Terrestrial Effects: A Curriculum Guide for Teachers, Grades 7–12. Space Environment Laboratory, National Oceanic and Atmospheric Administration, 1993. I. Dabbura. Gradient descent algorithm and its variants, December 21 2017. URL https://towardsdatascience.com/gradient-descent-algorithm-and-its-variants-10f652806a3. towards Data Science. Lore Goetschalckx, Alex Andonian, and Johan Wagemans. Generative adversarial networks un- lock new methods for cognitive science. Trends in Cognitive Sciences, September 2021. doi: 10.1016/j.tics.2021.07.002. George Ellery Hale. On the probable existence of a magnetic field in sun-spots. Astrophysical Journal, 28:315–343, 1908. Rom Harré. Models in science. Physics Education, 1978. URL https://api.semanticscholar. org/CorpusID:117952040. Yoshua Bengio Ian Goodfellow and Aaron Courville. Deep learning. The MIT Press, London, 2016. James B Kaler. Stars and their spectra: an introduction to the spectral sequence. Cambridge University Press, 2011. Sandesh Kumar. Redes neuronales de convolución en pocas palabras, julio 5 2020. URL https: //www.herevego.com/redes-neuronales-de-convolucion/. Home; Sandesh Kumar. Joan Manuel Bullon Lahuerta and Ma Ángeles del Castillo Alarcos. Observación solar. APEA, Aso- ciación para la Enseñanza de la Astronomía, 2010. Maud Langlois, Gil Moretto, Kit Richards, Steve Hegwer, and Thomas R Rimmele. Solar multi- conjugate adaptive optics at the dunn solar telescope: preliminary results. In Advancements in Adaptive Optics, volume 5490, pages 59–66. SPIE, 2004. McCulloch, JY Pitts, Lettvin, PD Wall, and B Howland. Inhibition of impulses travelling in the primary afferent dorsal column fibres of cat spinal cord. In Fed Proc, volume 13, page 87, 1954. Dale A Ostlie and Bradley W Carroll. An introduction to modern astrophysics. Addison-Wesley Read- ing, MA, USA, 2007. Dina Prialnik. An Introduction to the Theory of Stellar Structure and Evolution. CAMBRIDGE, Cambridge, 2010. Eric Ronald Priest. Solar magnetohydrodynamics, volume 21. Springer Science & Business Media, 2012. David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. nature, 323(6088):533–536, 1986. Karl Schwarzschild. Gesammelte Werke Collected Works: Volume 1, volume 1. Springer-Verlag, 2013. Sergiy Shelyag, Peter Keys, Mihalis Mathioudakis, and Francis P Keenan. Vorticity in the solar photosphere. Astronomy & Astrophysics, 526:A5, 2011. K Wilhelm, P Lemaire, W Curdt, U Schühle, E Marsch, AI Poland, SD Jordan, RJ Thomas, DM Has- sler, MCE Huber, et al. First results of tide sumer telescope and spectrometer on soho: I. spectra and spectroradiometry. The First Results from SOHO, pages 75–104, 1997. William Falcon et al. pytorch-lightning.readthedocs.io, Enero de 2024. Friedrich Wöger, Thomas Rimmele, Andrew Ferayorni, Andrew Beard, Brian S Gregory, Predrag Sekulic, and Steven L Hegwer. The daniel k. inouye solar telescope (dkist)/visible broadband imager (vbi). Solar Physics, 296:1–25, 2021. Harold Zirin. Astrophysics of the Sun. Cambridge University Press, Cambridge, UK, 1988. ISBN 978-0521343540. Abdurrahman Öcal and Lale Özbakır. Supervised deep convolutional generative adversarial net- works. Neurocomputing, 449:389–398, 2021. ISSN 0925-2312. doi: https://doi.org/10.1016/ j.neucom.2021.03.125. URL https://www.sciencedirect.com/science/article/pii/ S0925231221005178. W Dean Pesnell, B J. Thompson, and PC Chamberlin. The solar dynamics observatory (SDO). Springer, 2012. |
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viii, 66 páginas |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Astronomía |
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Facultad de Ciencias |
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Bogotá, Colombia |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Vargas Domínguez, Santiagobe22c53e96dc3348c6f6be91604e15deShelyag, Sergiy4000ca5662ea578f7671b324d96837b5600Morales Suarez, Germain Nicolas36079b2b6cef3d1084b986f39896e13d2024-04-29T19:54:42Z2024-04-29T19:54:42Z2023https://repositorio.unal.edu.co/handle/unal/85995Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEl presente trabajo se enmarca en las aplicaciones de las redes neuronales profundas para el modelamiento de los fenómenos presentes en la fotósfera solar. La investigación propuesta se basa en la construcción de red neuronal convolucional 3D profunda de tipo generativa, DCGAN por sus siglas en ingles, haciendo uso de las módulos de inteligencia artificial de Python como Pytorch para arquitectura de la de red neuronal. Se pretende entrenar una red neuronal capaz de generar grupos de cubos de una alta similitud con cubos de entrenamiento, dichos cubos corresponden a magnitudes físicas de la fotósfera solar tales como densidad, campo magnético, velocidad del plasma, temperatura, entre otras, obtenidas del código de simulación MURaM. Codigo de simulación desarrollado por el grupo Solar-MHD de instituto Max Planck desarrollado entre el 2001-2005 con la finalidad de generar simulaciones realistas de procesos de magneto-convección y actividades magneticas, que tienen caso sobre la zona convectiva del sol, el presente trabajo busca tomar sus resultado y tomarlos como datos de entrenamiento para la red neuronal generando datos nuevos con una similitud de manera visual y en los apartados físicos, posteriormente realizar una comparativa entre los resultados y los datos de entrenamiento, se proponen los retos para usar estas herramientas en el estudio de la fotósfera solar, tubos de flujo y poros. (Texto tomado de la fuente).The present work is framed in the applications of deep neural networks for the modeling of the phenomena present in the solar photosphere. The proposed research is based on the construction of a 3D deep generative convolutional neural network, DCGAN, using Python artificial intelligence modules such as Pytorch for neural network architecture. It is intended to train a neural network capable of generating groups of cubes of high similarity with training cubes, these cubes correspond to physical quantities of the solar photosphere such as density, magnetic field, plasma velocity, temperature, among others, obtained from the simulation code MURaM. Simulation code developed by the Solar-MHD group of the Max Planck Institute developed between 2001-2005 with the purpose of generating realistic simulations of magneto-convection processes and magnetic activities, which have an effect on the convective zone of the sun, The present work seeks to take its results and take them as training data for the neural network generating new data with a similarity in a visual way and in the physical sections, then make a comparison between the results and the training data, the challenges are proposed to use these tools in the study of the solar photosphere, flux tubes and pores.MaestríaMagíster en Ciencias - AstronomíaAstrofísica solarviii, 66 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - AstronomíaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá520 - Astronomía y ciencias afines::522 - Técnicas, procedimientos, aparatos, equipos, materialesDCGANFotósferaMHDDeep learningCNNPytorchPhotosphereAprendizaje profundoFotosferaastronomía solardeep learningphotospheresolar astronomyAplicación de técnicas de Deep Learning en modelamiento y observación de la fotósfera solarApplication of Deep Learning techniques in modeling and observation of the photosphereTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMM. Schüssler F. Cattaneo T. Emonet A. Vögler, S. Shelyag1 and T. Linde. Simulations of magneto- convection in the solar photosphere equations, methods, and results of the muram code. As- tronomy & Astrophysics, II:8–10, 2004.C. M. Bishop. Neural networks for pattern recognition. Astronomy & Astrophysics, II:0–498, 1996.Roger P Briggs. Solar Physics and Terrestrial Effects: A Curriculum Guide for Teachers, Grades 7–12. Space Environment Laboratory, National Oceanic and Atmospheric Administration, 1993.I. Dabbura. Gradient descent algorithm and its variants, December 21 2017. URL https://towardsdatascience.com/gradient-descent-algorithm-and-its-variants-10f652806a3. towards Data Science.Lore Goetschalckx, Alex Andonian, and Johan Wagemans. Generative adversarial networks un- lock new methods for cognitive science. Trends in Cognitive Sciences, September 2021. doi: 10.1016/j.tics.2021.07.002.George Ellery Hale. On the probable existence of a magnetic field in sun-spots. Astrophysical Journal, 28:315–343, 1908.Rom Harré. Models in science. Physics Education, 1978. URL https://api.semanticscholar. org/CorpusID:117952040.Yoshua Bengio Ian Goodfellow and Aaron Courville. Deep learning. The MIT Press, London, 2016.James B Kaler. Stars and their spectra: an introduction to the spectral sequence. Cambridge University Press, 2011.Sandesh Kumar. Redes neuronales de convolución en pocas palabras, julio 5 2020. URL https: //www.herevego.com/redes-neuronales-de-convolucion/. Home; Sandesh Kumar.Joan Manuel Bullon Lahuerta and Ma Ángeles del Castillo Alarcos. Observación solar. APEA, Aso- ciación para la Enseñanza de la Astronomía, 2010.Maud Langlois, Gil Moretto, Kit Richards, Steve Hegwer, and Thomas R Rimmele. Solar multi- conjugate adaptive optics at the dunn solar telescope: preliminary results. In Advancements in Adaptive Optics, volume 5490, pages 59–66. SPIE, 2004.McCulloch, JY Pitts, Lettvin, PD Wall, and B Howland. Inhibition of impulses travelling in the primary afferent dorsal column fibres of cat spinal cord. In Fed Proc, volume 13, page 87, 1954.Dale A Ostlie and Bradley W Carroll. An introduction to modern astrophysics. Addison-Wesley Read- ing, MA, USA, 2007.Dina Prialnik. An Introduction to the Theory of Stellar Structure and Evolution. CAMBRIDGE, Cambridge, 2010.Eric Ronald Priest. Solar magnetohydrodynamics, volume 21. Springer Science & Business Media, 2012.David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. nature, 323(6088):533–536, 1986.Karl Schwarzschild. Gesammelte Werke Collected Works: Volume 1, volume 1. Springer-Verlag, 2013.Sergiy Shelyag, Peter Keys, Mihalis Mathioudakis, and Francis P Keenan. Vorticity in the solar photosphere. Astronomy & Astrophysics, 526:A5, 2011.K Wilhelm, P Lemaire, W Curdt, U Schühle, E Marsch, AI Poland, SD Jordan, RJ Thomas, DM Has- sler, MCE Huber, et al. First results of tide sumer telescope and spectrometer on soho: I. spectra and spectroradiometry. The First Results from SOHO, pages 75–104, 1997.William Falcon et al. pytorch-lightning.readthedocs.io, Enero de 2024.Friedrich Wöger, Thomas Rimmele, Andrew Ferayorni, Andrew Beard, Brian S Gregory, Predrag Sekulic, and Steven L Hegwer. The daniel k. inouye solar telescope (dkist)/visible broadband imager (vbi). Solar Physics, 296:1–25, 2021.Harold Zirin. Astrophysics of the Sun. Cambridge University Press, Cambridge, UK, 1988. ISBN 978-0521343540.Abdurrahman Öcal and Lale Özbakır. Supervised deep convolutional generative adversarial net- works. Neurocomputing, 449:389–398, 2021. ISSN 0925-2312. doi: https://doi.org/10.1016/ j.neucom.2021.03.125. URL https://www.sciencedirect.com/science/article/pii/ S0925231221005178.W Dean Pesnell, B J. Thompson, and PC Chamberlin. The solar dynamics observatory (SDO). Springer, 2012.EstudiantesInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85995/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1023010074.2024.pdf1023010074.2024.pdfTesis de Maestría en Ciencias - Astronomíaapplication/pdf42954073https://repositorio.unal.edu.co/bitstream/unal/85995/2/1023010074.2024.pdf0717c9104ad22189530fbf08ebfafd2bMD52THUMBNAIL1023010074.2024.pdf.jpg1023010074.2024.pdf.jpgGenerated Thumbnailimage/jpeg6667https://repositorio.unal.edu.co/bitstream/unal/85995/3/1023010074.2024.pdf.jpgbeae9c641639c32d8cf6160100089646MD53unal/85995oai:repositorio.unal.edu.co:unal/859952024-08-24 23:12:29.462Repositorio Institucional Universidad Nacional de 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