[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127656-en":3,"doc-seo-127656-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127656,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Application of Machine Learning and Artificial Intelligence to Extend EFIT Equilibrium Reconstruction - Research Overview","Recent progress in applying machine learning and artificial intelligence algorithms to improve EFIT equilibrium reconstruction for fusion data analysis is presented. A device-independent portable core equilibrium solver enables equilibrium computation across multiple tokamaks. A large EFIT database of DIII-D magnetic, Motional-Stark Effect (MSE), and kinetic reconstruction data supports EFIT model-order-reduction surrogate development, including a trained neural-network surrogate with encouraging results. Additional work includes a Gaussian-Process Bayesian framework, plus a 3D perturbed-equilibrium database for 3D-MOR models.","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nApplication of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction  \nPermalink  \n[https://escholarship.org/uc/item/7qt479zq](https://escholarship.org/uc/item/7qt479zq)  \nJournal  \nPlasma Physics and Controlled Fusion, 64(7)  \nISSN  \n0741-3335  \nAuthors  \nLao, LL  \nKruger, SAkcay, Cet al.  \nPublication Date  \n2022-07-01  \nDOI  \n10.1088/1361-6587/ac6fff  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution  \nNonCommercial License, availalbe at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nApplication of Machine Learning and Artificial Intelligence to Extend EFIT Equilibrium Reconstruction  \nL.L. Lao 1* , S. Kruger2 , C. Akcay 1 , P. Balaprakash3 , T.A. Bechtel 1,4 , E. Howell2 , J. Koo3 , J. Leddy2 , M. Leinhauser5 , Y.Q. Liu 1 , S. Madireddy3 , J. McClenaghan 1 , D. Orozco 1 , A. Pankin6 , D. Schissel 1 , S. Smith 1 , X. Sun 1,4 , and S. Williams7  \n1General Atomics, San Diego, California, USA 2TechX, Boulder, Colorado, USA  \n3Argon National Laboratory, Lemont, Illinois, USA  \n4Oak Ridge Associated University, Oak Ridge, Tennessee, USA 5University of Delaware, Newark, Delaware, USA  \n6Princeton Plasma Physics Laboratory, Princeton, New Jersey, USA 7Lawrence Berkeley National Laboratory, Berkeley, California, USA  \n*[Contact author: Lao@Fusion.gat.com](Contact author: Lao@Fusion.gat.com)  \nAbstract ¾ Recent progress in the application of machine learning (ML) / artificial intelligence (AI) algorithms to improve EFIT equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, Motional-Stark Effect (MSE) , and kinetic reconstruction data has been generated for developments of EFIT ModelOrder-Reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. A neural-network (NN) MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian-Process (GP) Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the MARS-F code for developments of 3D-MOR surrogate models.  \nKeywords ¾ Tokamak equilibrium reconstruction, machine learning, artificial intelligence, Gaussian Process, Model Order Reduction, neural network, 3D perturbed equilibrium  \nI. INTRODUCTION AND OVERVIEW  \nReconstruction of experimental axisymmetric magnetohydrodynamic (MHD) equilibria is fundamental to tokamak research and operation and is an important part of fusion data analysis and plasma control. Equilibrium reconstruction provides essential magnetic geometry and current and pressure profiles information necessary to support tokamak operation and data analysis, and has contributed to several major discoveries of tokamak physics such as the experimental validation of theoretically predicted b stability limits [1] and the negative central-shear operating regime [2,3] .  \nThe equilibrium reconstruction and fitting code EFIT is widely used in many tokamaks toreconstruct experimental MHD equilibria. These include reconstructions at the DIII-D, C-MOD, JET, HIT, START, MAST, KSTAR, JT-60U, NSTX, TORE SUPRA, HT-7, HL-2A, EAST , and QUEST devices [4-26] . EFIT reconstructs equilibria by solving the Grad-Shafranov (GS) equation [27 , 28] while approximately conserving the available experimental ","cbCaigfRdT9imzsZ","https://ap.wps.com/l/cbCaigfRdT9imzsZ","pdf",2028728,2,1,44,"English","en",105,"# Introduction and Overview\n## Role of equilibrium reconstruction in tokamak research\n## EFIT method and governing Grad-Shafranov formulation\n## Machine learning and AI approaches for fusion equilibria\n## Scope: extending EFIT equilibrium reconstruction with ML/AI","[{\"question\":\"What problem does EFIT equilibrium reconstruction address in tokamak research?\",\"answer\":\"It reconstructs axisymmetric magnetohydrodynamic equilibria by inferring current-source information from external and internal plasma measurements while satisfying physics constraints.\"},{\"question\":\"How does the paper extend EFIT using machine learning and artificial intelligence?\",\"answer\":\"It introduces a device-independent portable core equilibrium solver (EFIT-AI), builds large EFIT datasets for surrogate modeling, and trains/validates ML-based model-order-reduction surrogates.\"},{\"question\":\"What surrogate modeling techniques are highlighted in the document?\",\"answer\":\"A neural-network model-order-reduction surrogate is trained and tested using magnetically reconstructed datasets. It also discusses progress with a Gaussian-Process Bayesian framework and developments for 3D-MOR surrogate models using 3D perturbed-equilibrium databases.\"}]","Application of Machine Learning and Artificial Intelligence to Extend EFIT Equilibrium Reconstruction - Research Overview | PDF",1785940533,111,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"application-of-machine-learning-and-artificial-intelligence-to-extend-efit-equilibrium-reconstruction-research-overview","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-and-artificial-intelligence-to-extend-efit-equilibrium-reconstruction-research-overview/127656/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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 EFIT equilibrium reconstruction address in tokamak research?","Question",{"text":76,"@type":77},"It reconstructs axisymmetric magnetohydrodynamic equilibria by inferring current-source information from external and internal plasma measurements while satisfying physics constraints.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the paper extend EFIT using machine learning and artificial intelligence?",{"text":81,"@type":77},"It introduces a device-independent portable core equilibrium solver (EFIT-AI), builds large EFIT datasets for surrogate modeling, and trains/validates ML-based model-order-reduction surrogates.",{"name":83,"@type":74,"acceptedAnswer":84},"What surrogate modeling techniques are highlighted in the document?",{"text":85,"@type":77},"A neural-network model-order-reduction surrogate is trained and tested using magnetically reconstructed datasets. It also discusses progress with a Gaussian-Process Bayesian framework and developments for 3D-MOR surrogate models using 3D perturbed-equilibrium databases.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]