20.500.12556/RUL-180118 Accelerating Particle-in-Cell simulations in Tokamak Scrape-off Layer using segmented surrogate models 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. fusion energy plasma potential machine learning surrogate modeling extreme gradient boosting method fuzijska energija plazemski potencial strojno učenje nadomestno modeliranje metoda ekstremnega gradientnega ojačevanja true false true Angleški jezik Slovenski jezik Članek v reviji 2026-03-03 10:32:55 2026-03-03 10:33:03 2026-03-04 04:18:25 0000-00-00 00:00:00 2026 0 0 14 str. Vol. 172, art. 114332 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 621.039:004.85 0952-1976 10.1016/j.engappai.2026.114332 270229507 Vukasinovic_..._Accelerating_Particle....pdf Vukasinovic_..._Accelerating_Particle....pdf 1 6B2D9D54E465018CA916063B39283B95 3a6be995739fce624688ab5fec1b81a6deca897e37fd9389b9dbb6d46defd0ab d12790b2-16e3-11f1-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=229227 https://www.sciencedirect.com/science/article/pii/S0952197626006135 1 a3890257-16e3-11f1-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=229225 Fakulteta za strojništvo 0 0 0