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20242026
most citedFull Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks

2 citations · 4 across the 5 of their papers we have counts for

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physics.plasm-ph2026

Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks

Daniel Gaytan-Villarreal, Aiken Xie, Tu Pham +5

Real time plasma state estimation for control of Tokamak devices are challenging due to the stringent latency requirements of the plasma control system (PCS). We present an end-to-…

cs.LG2026

Offline Reinforcement Learning for Rotation Profile Control in Tokamaks

Rohit Sonker, Hiro Josep Farre Kaga, Jiayu Chen +5

Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved. One such challen…