2 citations · 4 across the 5 of their papers we have counts for
5 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-…
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…
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…
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks
Ian Char, Youngseog Chung, Joseph Abbate +2
Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics…