2 citations · 2 across the 5 of their papers we have counts for
6 papers
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-…
Bayesian Optimization with Rich Auxiliary Information via LLMs
Tejus Gupta, Efe Mert Karagözlü, Rohit Sonker +2
Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet it typically reduces each expensive experiment to an input and its objective value, disc…
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…
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…
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…