[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120595-en":3,"doc-seo-120595-105":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},120595,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Developing machine learning facilitated pedestal models - Conference pre-print","Conference manuscript summarizing recent work on a project developing machine learning (ML) facilitated pedestal models. The effort is organized into three branches: surrogate modeling for pedestal magnetohydrodynamics, reduced pedestal transport model development using ML, and data-driven methods that learn corrections between numerical predictions and experimental observations. Progress includes newly published proof-of-principle acceleration for pedestal MHD stability evaluations and next-step activities extending beyond that baseline.","This document is downloaded from the VTT Research Information Portal  \n[https://cris.vtt.fi](https://cris.vtt.fi)  \nVTT Technical Research Centre of Finland  \nDeveloping machine learning facilitated pedestal models  \nJärvinen, Aaro; Bruncrona, Amanda; Jordan, Daniel; Kit, Adam; Niemelä, Anna; Dunne, M. ; Frassinetti, L. ; Nyström, Hampus; Hatch, David; Schmidt, Joseph; Stephens, Cole; Leppin, Leonhard; Menkovski, Vlado; Poels, Y. R.J. ; Saarelma, Samuli; Zanisi, Lorenzo; Wiesen, Sven  \nPublished: 01/01/2025  \nDocument Version  \nPublisher's final version  \nLink to publication  \nPlease cite the original version:  \nJärvinen, A. , Bruncrona, A. , Jordan, D. , Kit, A. , Niemelä, A. , Dunne, M. , Frassinetti, L. , Nyström, H. , Hatch, D. , Schmidt, J. , Stephens, C. , Leppin, L. , Menkovski, V. , Poels, Y. R. J. , Saarelma, S. , Zanisi, L. , & Wiesen, S.(2025) . Developing machine learning facilitated pedestal models. Paper presented at 30th IAEA Fusion Energy Conference, IAEA FEC 2025, Chengdu, China. [https://conferences.iaea.org/event/392/contributions/35919/](https://conferences.iaea.org/event/392/contributions/35919/)  \nVTT  \n[https://www.vttresearch.com](https://www.vttresearch.com)  \nVTT Technical Research Centre of Finland Ltd  \n[P.O. box 1000](P.O. box 1000)[ ](P.O. box 1000)[FI-02044 VTT](FI-02044 VTT)[ ](FI-02044 VTT)Finland  \nBy using VTT Research Information Portal you are bound by the following Terms & Conditions.  \nI have read and I understand the following statement:  \nThis document is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of this document is not permitted, except duplication for research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered for sale.  \nDownload date: 08. Mar. 2026  \nCONFERENCE PRE-PRINT  \nDEVELOPING MACHINE LEARNING FACILITATED PEDESTAL MODELS  \nA.E. JÄRVINEN, A.M. BRUNCRONA, D. JORDAN, A. KIT, A. NIEMELÄ VTT Technical Research Centre of Finland  \nEspoo, Finland  \nEmail: [aaro.jarvinen@vtt.fi](aaro.jarvinen@vtt.fi)  \nM. DUNNE  \nMax-Planck-Institut für Plasmaphysik Garching, Germany  \nL. FRASSINETTI, H. NYSTRÖM  \nDivision of Fusion Plasma Physics, KTH Royal Institute of Technology Stockholm, Sweden  \nD.R. HATCH, J. SCHMIDT, C. STEPHENS  \nInstitute for Fusion Studies, University of Texas at Austin Austin, Texas, USA  \nL. LEPPIN  \nOden Institute, University of Texas at Austin Austin, Texas, USA  \nV. MENKOVSKI  \nEindhoven University of Technology, Mathematics and Computer Science Eindhoven, the Netherlands  \nY.R.J. POELS  \nSwiss Plasma Center, EPFL Lausanne, Switzerland  \nS. SAARELMA, L. ZANISI UK Atomic Energy Authority Abingdon, UK  \nS. WIESEN  \nDutch Institute for Fundamental Energy Research – DIFFER Eindhoven, the Netherlands  \nTHE ASDEX UPGRADE TEAM*  \nSee the author list of ‘Overview of ASDEX upgrade results in view of ITER and DEMO’ by H Zohm et al. Nuclear Fusion 64 (2024) 112001.  \nJET CONTRIBUTORS  \nSee the author list of ’Overview of T and D-T results in JET with ITER-like wall’ by CF Maggi et al. Nuclear 64 (2024) 112012.  \nAbstract  \nThis conference manuscript provides an overview of recent activities in a project developing machine learning (ML) facilitated pedestal models. The project is divided to three branches, consisting of surrogate modelling techniques for pedestal magnetohydrodynamics, development of reduced pedestal transport models with ML methods, as well as data-driven methods to learn corrections for the remaining gap between numerical predictions and experimental observations. A proof-of-principle model for accelerating pedestal MHD stability evaluations has been recently published, and the next step activities to go beyond this proof-of-principle are detailed. First proof-of-principle models are emerging from the part of the project developing surrogate models for local, linear pedestal gyrokinetic evaluations b","cbCaimgH18HpGR6w","https://ap.wps.com/l/cbCaimgH18HpGR6w","pdf",787686,1,9,"English","en",105,"# Abstract\n# Introduction\n## Pedestal optimization and predictive modeling\n## Reduced models and stability workflow\n## Motivations for data-driven extensions","[{\"question\":\"How is the project developing ML-facilitated pedestal models organized?\",\"answer\":\"It is divided into three branches: surrogate modeling for pedestal MHD, reduced pedestal transport models using ML, and data-driven correction methods bridging gaps between simulations and experiments.\"},{\"question\":\"What role do reduced transport models and linear MHD solvers play?\",\"answer\":\"Reduced transport models generate pedestal profiles under assumed edge transport barrier widths, while a linear MHD stability solver (e.g., MISHKA or ELITE) computes the stability envelope to find profiles satisfying transport and MHD constraints.\"},{\"question\":\"What progress is reported beyond the first proof-of-principle work?\",\"answer\":\"A proof-of-principle model has been published to accelerate pedestal MHD stability evaluations, and the manuscript details next-step activities to go beyond this baseline.\"}]","Developing machine learning facilitated pedestal models - 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