[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120577-en":3,"doc-seo-120577-105":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":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},120577,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Developing machine learning facilitated pedestal models - Conference pre-print","This conference manuscript surveys recent work on a project developing machine learning (ML) facilitated pedestal models for fusion plasmas. The effort is organized into three branches: surrogate modelling for pedestal magnetohydrodynamics, ML-based reduced pedestal transport models, and data-driven correction learning to close the gap between numerical predictions and experimental observations. It summarizes emerging proof-of-principle models for accelerating pedestal MHD stability evaluations and outlines next-step activities beyond the initial demonstrations, including pathways that combine physics models with experimental observations.","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: 14. Feb. 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","cbCainkZhRawzctt","https://ap.wps.com/l/cbCainkZhRawzctt","pdf",787687,1,9,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the focus of the conference manuscript?\",\"answer\":\"It provides an overview of recent activities in a project developing ML-facilitated pedestal models for fusion research, including surrogate, reduced transport, and data-driven correction approaches.\"},{\"question\":\"How is the project structured?\",\"answer\":\"The work is divided into three branches: surrogate modelling for pedestal magnetohydrodynamics, reduced pedestal transport models using ML methods, and data-driven methods that learn corrections to address remaining discrepancies.\"},{\"question\":\"What proof-of-principle result does the manuscript mention?\",\"answer\":\"It notes a proof-of-principle model for accelerating pedestal MHD stability evaluations and describes next-step activities to go beyond the initial demonstration.\"}]","Developing machine learning facilitated pedestal models - 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