136 citations · 187 across the 7 of their papers we have counts for
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physics.plasm-ph2022★ 12 cited
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…
physics.plasm-ph2022★ 6 cited
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…