136 citations · 523 across the 15 of their papers we have counts for
15 papers
Machine learning methods for modelling local, linear gyrokinetic simulations of MAST-U pedestal turbulence
Anna Niemelä, Daniel Jordan, Aaro Järvinen +8
Gyrokinetic (GK) stability strongly influences the performance of high-confinement-mode pedestals in spherical tokamak plasmas. High-fidelity gyrokinetic codes such as GENE can mod…
Reconstructions of electron-temperature profiles from EUROfusion Pedestal Database using turbulence models and machine learning
L. -P. Turica, A. R. Field, L. Frassinetti +3
This study uses plasma-profile data from the EUROfusion pedestal database, focusing on the electron-temperature and electron-density profiles in the edge region of H-mode ELMy JET…
The JET hybrid H-mode scenario from a pedestal turbulence perspective
L. A. Leppin, T. Görler, L. Frassinetti +4
Turbulent transport is a decisive factor in determining the pedestal structure of H-modes. Here, we present the first comprehensive characterization of gyrokinetic turbulent transp…
Deep or Not Deep: Supervised Learning Approaches to Modeling the Pedestal Density
Adam Kit, Aaro Jaervinen, Lorenzo Frassinetti +2
Pedestal is the key to conventional high performance plasma scenarios in tokamaks. However, high fidelity simulations of pedestal plasmas are extremely challenging due to the multi…
Developing Deep Learning Algorithms for Inferring Upstream Separatrix Density at JET
A. Kit, A. Jaervinen, S. Wiesen +2
Predictive and real-time inference capability for the upstream separatrix electron density, , is essential for design and control of core-edge integrated plasma sc…
Microtearing modes as the source of magnetic fluctuations in the JET pedestal
D. R. Hatch, M. Kotschenreuther, S. M. Mahajan +15
We report on a detailed study of magnetic fluctuations in the JET pedestal, employing basic theoretical considerations, gyrokinetic simulations, and experimental fluctuation data,…