4 papers
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…
Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
Yang Fu, Haomin Bao, Rohit Sonker +4
Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly…
Interpreting AI for Fusion: an application to Plasma Profile Analysis for Tearing Mode Stability
Hiro J Farre-Kaga, Andrew Rothstein, Rohit Sonker +6
AI models have demonstrated strong predictive capabilities for various tokamak instabilities--including tearing modes (TM), ELMs, and disruptive event--but their opaque nature rais…
Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Rohit Sonker, Alexandre Capone, Andrew Rothstein +3
Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously c…