8 citations · 8 across the 3 of their papers we have counts for
3 papers
Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning
G. F. Subbotin, D. I. Sorokin, M. R. Nurgaliev +9
Precise control of plasma shape and position is essential for stable tokamak operation and achieving commercial fusion energy. Traditional control methods rely on equilibrium recon…
Reconstructing the Plasma Boundary with a Reduced Set of Diagnostics
M. S. Stokolesov, M. R. Nurgaliev, I. P. Kharitonov +4
This study investigates the feasibility of reconstructing the last closed flux surface (LCFS) in the DIII-D tokamak using neural network models trained on reduced input feature set…
In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss
Yuri Kuratov, Aydar Bulatov, Petr Anokhin +3
This paper addresses the challenge of processing long documents using generative transformer models. To evaluate different approaches, we introduce BABILong, a new benchmark design…