Accelerating Particle-in-Cell simulations in Tokamak Scrape-off Layer using segmented surrogate models

Vukašinović, Nikola (author), Urbas, Uroš (author), Kos, Leon (author), Vasileska, Ivona (author)

Achieving sustainable fusion energy critically depends on accurately modeling complex plasma dynamics within tokamak reactors, particularly in the Scrape-off Layer (SOL), where heat and particles directly interact with reactor walls, influencing reactor performance and component longevity. Particle-in-Cell (PIC) simulations, although highly accurate, are computationally expensive and time-consuming, limiting their use for iterative design and real-time control. We employ an Extreme Gradient Boosting (XGBoost)-based surrogate model to efficiently predict plasma potential along the tokamak SOL using data from PIC simulations under varying operating conditions. The machine learning (ML) approach integrates physics-informed segmentation of the spatial modeling domain, distinguishing sharply between sheath regions and the quasineutral bulk plasma. This segmentation substantially enhances the surrogate model’s predictive accuracy to localized physical phenomena, a marked improvement over traditional global modeling strategies. We utilize XGBoost regression with hyperparameter optimization achieved through a tailored leave-one-curve-out (LOCO) validation method, ensuring robust generalization to previously unseen plasma conditions. We found that a global model with segmented consideration of the spatial dimension based on boundary plasma physics captures localized behaviors more accurately. This segmented approach leads to a mean absolute percentage error (MAPE) of 3.2%, outperforming other methods. The main engineering application of this approach is the significant reduction in computational resources and simulation time required for fusion reactor design and real-time plasma control. This allows rapid iterative design, improved operational decision-making, and potentially extends the operational lifetime of reactor components.

• Language: English
• Keywords: fusion energy, plasma potential, machine learning, surrogate modeling, extreme gradient boosting method
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 14 str.
• Numbering: Vol. 172, art. 114332
• PID: 20.500.12556/RUL-180118
• UDC: 621.039:004.85
• ISSN on article: 0952-1976
• DOI: 10.1016/j.engappai.2026.114332
• COBISS.SI-ID: 270229507
• Publication date in RUL: 03.03.2026

Record is a part of a journal

• Title: Engineering applications of artificial intelligence
• Shortened title: Eng. appl. artif. intell.
• Publisher: Pineridge Press
• ISSN: 0952-1976
• COBISS.SI-ID: 25396224

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: fuzijska energija, plazemski potencial, strojno učenje, nadomestno modeliranje, metoda ekstremnega gradientnega ojačevanja