{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/applications-of-deep-learning-to-nuclear","title":"Applications of Deep Learning to Nuclear Fusion Research","arxiv_id":"1811.00333","date":"2018-11-01","proceeding":null,"authors":["Diogo R. Ferreira on behalf of JET Contributors"],"abstract":"Nuclear fusion is the process that powers the sun, and it is one of the best\nhopes to achieve a virtually unlimited energy source for the future of\nhumanity. However, reproducing sustainable nuclear fusion reactions here on\nEarth is a tremendous scientific and technical challenge. Special devices --\ncalled tokamaks -- have been built around the world, with JET (Joint European\nTorus, in the UK) being the largest tokamak currently in operation. Such\ndevices confine matter and heat it up to extremely high temperatures, creating\na plasma where fusion reactions begin to occur. JET has over one hundred\ndiagnostic systems to monitor what happens inside the plasma, and each\n30-second experiment (or pulse) generates about 50 GB of data. In this work, we\nshow how convolutional neural networks (CNNs) can be used to reconstruct the 2D\nplasma profile inside the device based on data coming from those diagnostics.\nWe also discuss how recurrent neural networks (RNNs) can be used to predict\nplasma disruptions, which are one of the major problems affecting tokamaks\ntoday. Training of such networks is done on NVIDIA GPUs.","url_abs":"http://arxiv.org/abs/1811.00333v1","url_pdf":"http://arxiv.org/pdf/1811.00333v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"applications-of-deep-learning-to-nuclear","repo_url":"https://github.com/Veloc1tyE/Drift","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"diagnostic","task_name":"Diagnostic"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}