[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122060-en":3,"doc-seo-122060-105":30,"detail-sidebar-cat-0-en-105":83},{"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},122060,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Key feature identification of internal kink mode using machine learning","Internal kink mode is a critical factor limiting the stability of magnetically confined fusion devices, making its growth-rate drivers essential for reliable long-duration operation. This paper applies machine learning to identify key physical features affecting growth rate, training Random Forest and XGBoost models on numerical simulation data to achieve high prediction accuracy. Permutation and SHAP methods then provide systematic feature-importance analysis. Resistance, magnetic-axis pressure, viscosity, and plasma rotation emerge as the primary controlling features, with mechanisms linked to current and magnetic-field evolution, pressure-gradient driving, flow regulation, and rotation-induced shear.","TYPE Original Research PUBLISHED 29 October 2024 DOI 10.3389/fphy.2024.1476618  \nOPEN ACCESS  \nEDITED BY  \nPeter Manz,  \nUniversity of Greifswald, Germany  \nREVIEWED BY  \nShishir Purohit,  \nInstitute for Plasma Research (IPR), India Imran Iqbal,  \nNew York University, United States  \n*CORRESPONDENCE  \nTeng Zhou,  \n [zhouteng@hainanu.edu.cn](zhouteng@hainanu.edu.cn)  \nRECEIVED 06 August 2024  \nACCEPTED 16 October 2024  \nPUBLISHED 29 October 2024  \nCITATION  \nNing H, Lou S, Wu J and Zhou T (2024) Key feature identification of internal kink mode using machine learning.  \nFront. Phys. 12:1476618 .  \ndoi: 10.3389/fphy.2024.1476618  \nCOPYRIGHT  \n© 2024 Ning, Lou, Wu and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nKey feature identification of internal kink mode using machine learning  \nHongwei Ning 1,2, Shuyong Lou 3, Jianguo Wu 1 and Teng Zhou 4*  \n1School of Computer Science and Technology, Anhui University, Hefei, Anhui, China, 2College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, Anhui, China, 3College of Electronic and Optical Engineering and College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China, 4Mechanical and Electrical Engineering College, Hainan University, Haikou, Hainan, China  \nThe internal kink mode is one of the crucial factors affecting the stability of magnetically confined fusion devices. This paper explores the key features influencing the growth rate of internal kink modes using machine learning techniques such as Random Forest, Extreme Gradient Boosting (XGboost), Permutation, and SHapley Additive exPlanations (SHAP) . We conduct an indepth analysis of the significant physical mechanisms by which these key features impact the growth rate of internal kink modes. Numerical simulation data were used to train high-precision machine learning models, namely Random Forest and XGBoost, which achieved coefficients of determination values of 95.07% and 94.57%, respectively, demonstrating their capability to accurately predict the growth rate of internal kink modes. Based on these models, key feature analysis was systematically performed with Permutation and SHAP methods. The results indicate that resistance, pressure at the magnetic axis, viscosity, and plasma rotation are the primary features influencing the growth rate of internal kink modes. Specifically, resistance affects the evolution of internal kink modes by altering current distribution and magnetic field structure; pressure at the magnetic axis impacts the driving force of internal kink modes through the pressure gradient directly related to plasma stability; viscosity modifies the dynamic behavior of internal kink modes by regulating plasma flow; and plasma rotation introduces additional shear forces, affecting the stability and growth rate of internal kink modes. This paper describes the mechanisms by which these four key features influence the growth rate of internal kink modes, providing essential theoretical insights into the behavior of internal kink modes in magnetically confined fusion devices.  \nKEYWORDS  \nfeature importance, internal kink mode, Random Forest, XGboost, permutation, SHAP  \n1 Introduction  \nIn controlled nuclear fusion research, the stability of plasma is one of the core challenges for achieving long-term stable fusion discharges [1, 2] . The internal kink mode is a typical magnetohydrodynamic (MHD) instability that profoundly impacts the performance and safety of fusion devices [3, 4] . To achieve a more efficient and stable fus","cbCairGcTp8fBv8x","https://ap.wps.com/l/cbCairGcTp8fBv8x","pdf",2279199,1,15,"English","en",105,"# Introduction\n# Methodology and Machine Learning Models\n## Random Forest and XGBoost\n## Feature Importance with Permutation and SHAP\n# Results and Physical Mechanisms\n## Resistance\n## Magnetic-axis pressure\n## Viscosity\n## Plasma rotation\n# Conclusion","[{\"question\":\"Which features are found to be the most important for the growth rate?\",\"answer\":\"Resistance, pressure at the magnetic axis, viscosity, and plasma rotation are identified as the primary features controlling the internal kink mode growth rate, each with a described physical impact pathway.\"}]","Key feature identification of internal kink mode using machine learning | PDF",1785808610,38,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"key-feature-identification-of-internal-kink-mode-using-machine-learning","",{"@graph":36,"@context":77},[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/key-feature-identification-of-internal-kink-mode-using-machine-learning/122060/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Which features are found to be the most important for the growth rate?","Question",{"text":75,"@type":76},"Resistance, pressure at the magnetic axis, viscosity, and plasma rotation are identified as the primary features controlling the internal kink mode growth rate, each with a described physical impact pathway.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]