[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122583-en":3,"doc-seo-122583-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},122583,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Disruption Prediction Approaches Using Machine Learning Tools in Tokamaks - Overview","Machine learning is applied to predict, classify, and potentially avoid disruptive plasma events in tokamaks, where disruptions cause loss of confinement and can severely damage in-vessel components. The document reviews data-driven disruption alarm concepts built from large experimental datasets, highlighting evolution from basic disruption/non-disruption classification to proximity-to-operational-boundary detection for scheduled avoidance strategies. Algorithms for JET are surveyed, including traditional neural network prediction and manifold learning for mapping and visualization of plasma operational space.","Disruption prediction approaches using machine learning tools in  \nTokamaks  \nG. Sias1, B. Cannas1, S. Carcangiu1, A. Fanni1, A. Murari2, A. Pau3, and the JET Contributors*  \nEUROfusion Consortium, JET, Culham Science Centre, Abingdon, OX14 3DB, UK  \n1 Electrical and El  \n1Electronic Engineering Dept. -University of Cagliari, Piazza D’Armi, 09123, Cagliari, Italy.  \n2 Consorzio RFX (CNR, ENEA, INFN, Universita' di Padova, Acciaierie Venete SpA), 35127 Padova, Italy. 3Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Plasma Center, CH-1015 Lausanne, Switzerland.  \n* See the author list of “X. Litaudon et al 2017 Nucl. Fusion 57 102001  \nNuclear fusion is one of the best options to achieve a virtually limitless energy source in the future. However, sustaining burning plasma reactions is very challenging because disruptive events cause the loss of plasma confinement and damages to the tokamak machine. Thus, reproducing a nuclear fusion reaction on Earth represents an actual scientific and technical challenge. Next generation machines must be able to predict, mitigate or avoid the disruptions but the present understanding of the phenomenon has not yet gone so far as to provide a physical model describing their root causes. On theother hand, a large quantity of experimental disruption data exists. For these reasons, in the last decades, machine learning techniques have been applied to designing alarm systems both to mitigate or to actively avoid approaching disruptions. More recently, the disruption predictor concept has been evolving toward a more complex system, able to detect the proximity of the plasma state to the boundaries of the operational space free from disruptionsin order to schedule avoidance strategies rather than mitigation actions. Such a predictor should also be able to determine which type of disruption is about to occur in order to efficiently take suitable disruption avoidance actions. This paper reports an overview of machine learning algorithms as tools for disruption prediction and classification at JET, the world's largest operational plasma physics experiment, located at Culham Centre for Fusion Energy in Oxfordshire, UK. Both traditional neural network models for disruption prediction and manifold learning approaches asa tool for plasma operational space mapping and visualization, disruption prediction and classification, are described.  \n1. Introduction  \nTo this day, the tokamak is the most promising configuration for the magnetic confinement of plasma (a very hot gas of charged particles) in fusion devices. Disruptive events still represent one of the main concerns for the protection of in-vessel and electromagnetic components of large size tokamaks, imposing several constraints on the design of the next step experimental devices. Plasma disruptions are sudden losses of plasma control in tokamaks: a disruption occurs when an instability grows to the point where the plasma extinguishes, and the stored energy is quickly released on the surrounding vessel and plasma facing components. From the point of view of potential create damage to the device, two phases can be distinguished during the disruption evolution: the thermal and the current quenches. During the first one extremely high thermal loads can be deposited on the plasma facing components and more in general on the first wall. During the second one, large eddy currents can be induced in the vacuum vessel and surrounding structures, causing very harmful electro-mechanical stress. Moreover, during the current quench, the production of relativistic (runaway) electrons poses another threat to the integrity of the plasma facing components and the vacuum vessel. Disruptions will not only expose the tokamak first wall components and vacuum vessel to severe erosion, runaway electron, and thermo and electro-mechanical stresses, but they also will limit the accessible range of the operational parameters. Therefore, avoiding plasma disruptions or predict","cbCaijh0IqC97Pkw","https://ap.wps.com/l/cbCaijh0IqC97Pkw","pdf",697031,1,10,"English","en",105,"# Introduction\n## Disruptions and protection constraints\n## Data-driven disruption prediction with machine learning\n## Classification-based approaches\n## Moving toward operational-space mapping and disruption types","[{\"question\":\"Why are plasma disruptions a major concern in tokamaks?\",\"answer\":\"Disruptions involve sudden loss of plasma control, releasing stored energy and causing thermal and current quenches. They can damage the first wall and vacuum vessel, induce harmful electro-mechanical stress, and threaten runaway electron production.\"},{\"question\":\"What role does a disruption predictor play in tokamak operations?\",\"answer\":\"A predictor issues alarms with appropriate lead time for either mitigation or avoidance. It supports safer operation by helping schedule actions before disruptions fully develop.\"},{\"question\":\"How has disruption prediction advanced beyond simple classification?\",\"answer\":\"Beyond labeling events as disruptive or non-disruptive, newer concepts aim to detect how close the plasma state is to boundaries of the safe operational space. The document also emphasizes determining the disruption type so that more effective avoidance strategies can be applied.\"}]","Disruption Prediction Approaches Using Machine Learning Tools in Tokamaks - Overview | PDF",1785811475,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"disruption-prediction-approaches-using-machine-learning-tools-in-tokamaks-overview","",{"@graph":36,"@context":85},[37,54,68],{"@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/disruption-prediction-approaches-using-machine-learning-tools-in-tokamaks-overview/122583/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are plasma disruptions a major concern in tokamaks?","Question",{"text":75,"@type":76},"Disruptions involve sudden loss of plasma control, releasing stored energy and causing thermal and current quenches. They can damage the first wall and vacuum vessel, induce harmful electro-mechanical stress, and threaten runaway electron production.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does a disruption predictor play in tokamak operations?",{"text":80,"@type":76},"A predictor issues alarms with appropriate lead time for either mitigation or avoidance. It supports safer operation by helping schedule actions before disruptions fully develop.",{"name":82,"@type":73,"acceptedAnswer":83},"How has disruption prediction advanced beyond simple classification?",{"text":84,"@type":76},"Beyond labeling events as disruptive or non-disruptive, newer concepts aim to detect how close the plasma state is to boundaries of the safe operational space. 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