1 citations · 1 across the 3 of their papers we have counts for
4 papers
An Inverse Grad-Shafranov Neural Network Approach to Tokamak Magnetic Control
Allen M. Wang, Adriano Mele, Cosmas Heiß +12
A new approach to tokamak magnetic control enabling high-precision plasma shaping and novel real-time adaptability is experimentally demonstrated on the Tokamak a Configuration Var…
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
Allen M. Wang, Alessandro Pau, Cristina Rea +12
The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advance…
Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning
Allen M. Wang, Oswin So, Charles Dawson +3
The tokamak offers a promising path to fusion energy, but plasma disruptions pose a major economic risk, motivating considerable advances in disruption avoidance. This work develop…
Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors
Allen M. Wang, Darren T. Garnier, Cristina Rea
While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for th…