[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119435-en":3,"doc-seo-119435-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119435,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Using Machine Learning Algorithms to Detect Plasma Disruptions in Fusion Reactors - Master’s Thesis","This thesis investigates how machine learning algorithms can be used to predict disruptions in fusion reactors. Disruptions are critical because they interfere with a tokamak’s ability to confine plasma, leading to significant damage to both the structure and equipment. Since tokamaks and ITER are central to fusion reactor design, preventing such events is essential for protecting future energy production. The work uses bolometer data from the JET tokamak, training a 3-tier CNN-LSTM-Linear model while iterating hyperparameters and evaluating configurations via F1-scores, with suggestions for improvements and optimization.","ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ  \nΣΧΟΛΗ ΕΦΑΡΜΟΣΜΕΝΩΝΜΑΘΗΜΑΤΙΚΩΝΚΑΙ ΦΥΣΙΚΩΝ ΕΠΙΣΤΗΜΩΝ  \nΣΧΟΛΗ ΜΗΧΑΝΟΛΟΓΩΝ  \nΜΗΧΑΝΙΚΩΝ  \nΕΚΕΦΕ «ΔΗΜΟΚΡΙΤΟΣ»  \nΙΝΣΤΙΤΟΥΤΟΝΑΝΟΕΠΙΣΤΗΜΗΣ ΚΑΙΝΑΝΟΤΕΧΝΟΛΟΓΙΑΣ  \nΙΝΣΤΙΤΟΥΤΟ ΠΥΡΗΝΙΚΗΣΚΑΙ ΣΩΜΑΤΙΔΙΑΚΗΣΦΥΣΙΚΗΣ  \nΔιατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών  \n«Φυσική και Τεχνολογικές Εφαρμογές»  \nΤίτλος εργασίας  \nUsing Machine Learning Algorithms to Detect Plasma Disruptions in Fusion Reactors  \nΜΕΤΑΠΤΥΧΙΑΚΗ ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ  \nτου MAMALOUKAS PATROKLOS-MILTIADIS  \nΕπιβλέπων: ΚΟΜΙΝΗΣ ΙΩΑΝΝΗΣ  \nΑθήνα, 09 , 2022  \nΕυχαριστίες  \nΣτον Επιβλέποντα Καθηγητή Ιωάννη Κομίνη για την ευκαιρία που μου έδωσε να ασχοληθώ μεένα θέμα στην αιχμή της επιστήμης  \nΣτους Αριστείδη και Φώτη για την ανεκτίμητη βοήθεια και συμβουλές καθόλη την διάρκειαεκπόνησης της εργασίας  \nΣτην οικογένεια μου για την υπομονή που έδειξεΣτον κύριο Νίκο για την πολύτιμη υποστήριξηΣτην Nvidia για την αρχιτεκτονική Ampere  \nAbstract  \nThe aim of this thesis is to investigate how Machine Learning algorithms can be used to predict disruptions in fusion reactors. These events are called such because they disrupt a tokamak’s ability to confine plasma and cause a great deal of damage to the structure and equipment. Tokamaks are the leading design principle in fusion reactor design, so is ITER, the largest soon-to-be-built reactor ever conceived; thus, phenomena such as these must be prevented to the best of our abilities, to avoid compromising the future of energy production. Using bolometer data from the JET Tokamak reactor, we feed a 3-tier (Convolutional, LSTM and Linear Layers) Machine Learning model and iterate over sets of hyperparameters. Comparing different combinations of hyperparameters gives a qualitative perspective on the optimal configuration for our model and results are graded according to their f1-scores. Future improvements and optimizations are also suggested.  \nContents  \n1 Introduction .......................................................................................................................................... 6  \n2 Fusion Basics ......................................................................................................................................... 8  \n3 Plasma for Fusion ................................................................................................................................ 12  \n3.1 Confinement................................................................................................................................ 14  \n4 Fusion in practice ................................................................................................................................ 19  \n4.1 Reactor Designs ........................................................................................................................... 19  \n4.2 JET reactor and technical specifications ...................................................................................... 26  \n5 Disruptions .......................................................................................................................................... 28  \n5.1 Prediction .................................................................................................................................... 31  \n6 Artificial Intelligence ............................................................................................................................ 33  \n6.1 Neural Networks.......................................................................................................................... 34  \n6.2 Convolutional Neural Networks .................................................................................................. 42  \n6.3 Recurrent Neural networks ......................................................................................................... 45  \n7 Our practical application ..................................................................................................................... 49  \n7.1","cbCaielbP4d1acgw","https://ap.wps.com/l/cbCaielbP4d1acgw","pdf",5944296,1,96,"English","en",105,"# 1 Introduction\n# 2 Fusion Basics\n# 3 Plasma for Fusion\n## 3.1 Confinement\n# 4 Fusion in practice\n## 4.1 Reactor Designs\n## 4.2 JET reactor and technical specifications\n# 5 Disruptions\n## 5.1 Prediction\n# 6 Artificial Intelligence\n## 6.1 Neural Networks\n## 6.2 Convolutional Neural Networks\n## 6.3 Recurrent Neural networks\n# 7 Our practical application\n## 7.1 Data Roadmap\n## 7.2 Infrastructure\n## 7.3 Model Architecture\n## 7.4 Post Processing\n## 7.5 Result Interpretation\n# 8 Results\n# 9 Summary\n## 9.1 Descriptive Stats for Formula-criterion\n## 9.2 Descriptive Stats for Empirical-criterion\n## 9.3 Best Run\n# 10 Analysis and Conclusions\n# 11 Future research\n# 12 Appendix\n## 12.1 Toroidal Geometry","[{\"question\":\"What problem does the thesis address in fusion reactors?\",\"answer\":\"It focuses on predicting plasma disruptions in fusion reactors, events that disrupt a tokamak’s ability to confine plasma and can cause damage to the structure and equipment.\"},{\"question\":\"What data and model are used for the disruption prediction?\",\"answer\":\"The thesis uses bolometer data from the JET tokamak and trains a 3-tier machine learning model combining convolutional layers, LSTM, and linear layers.\"},{\"question\":\"How are model configurations evaluated in the study?\",\"answer\":\"Different hyperparameter combinations are compared, and the results are graded using F1-scores to assess which configuration is optimal.\"}]","Using Machine Learning Algorithms to Detect Plasma Disruptions in Fusion Reactors - Master’s Thesis | PDF",1785724276,242,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"using-machine-learning-algorithms-to-detect-plasma-disruptions-in-fusion-reactors-masters-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-algorithms-to-detect-plasma-disruptions-in-fusion-reactors-masters-thesis/119435/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in fusion reactors?","Question",{"text":76,"@type":77},"It focuses on predicting plasma disruptions in fusion reactors, events that disrupt a tokamak’s ability to confine plasma and can cause damage to the structure and equipment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and model are used for the disruption prediction?",{"text":81,"@type":77},"The thesis uses bolometer data from the JET tokamak and trains a 3-tier machine learning model combining convolutional layers, LSTM, and linear layers.",{"name":83,"@type":74,"acceptedAnswer":84},"How are model configurations evaluated in the study?",{"text":85,"@type":77},"Different hyperparameter combinations are compared, and the results are graded using F1-scores to assess which configuration is optimal.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]