38 citations · 63 across the 5 of their papers we have counts for
5 papers
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…
Parametric scaling of power exhaust in EU-DEMO alternative divertor simulations
A. E. Järvinen, L. Aho-Mantila, T. Lunt +3
Investigations of parametric scaling of power exhaust in the alternative divertor configuration (ADC) SOLPS-ITER simulation database of the EU-DEMO are conducted and compared to pr…
Bayesian approach for validation of runaway electron simulations
Aaro Järvinen, Tünde Fülöp, Eero Hirvijoki +3
Plasma-terminating disruptions in future fusion reactors may result in conversion of the initial current to a relativistic runaway electron beam. Validated predictive tools are req…
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…
Measuring the Electron Temperature and Identifying Plasma Detachment using Machine Learning and Spectroscopy
C. M. Samuell, A. G. Mclean, C. A. Johnson +2
A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network…