33 citations · 93 across the 22 of their papers we have counts for
6 papers · 1 filter
Exploration via Planning for Information about the Optimal Trajectory
Viraj Mehta, Ian Char, Joseph Abbate +5
Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying…
Greedy permanent magnet optimization
Alan A. Kaptanoglu, Rory Conlin, Matt Landreman
A number of scientific fields rely on placing permanent magnets in order to produce a desired magnetic field. We have shown in recent work that the placement process can be formula…
Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System
William Tang, Ge Dong, Jayson Barr +17
This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 N…
The DESC Stellarator Code Suite Part III: Quasi-symmetry optimization
Daniel Dudt, Rory Conlin, Dario Panici +1
The DESC stellarator optimization code takes advantage of advanced numerical methods to search the full parameter space much faster than conventional tools. Only a single equilibri…
The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations
Dario Panici, Rory Conlin, Daniel W. Dudt +2
3D equilibrium codes are vital for stellarator design and operation, and high-accuracy equilibria are also necessary for stability studies. This paper details comparisons of two th…
The DESC Stellarator Code Suite Part II: Perturbation and continuation methods
Rory Conlin, Daniel W. Dudt, Dario Panici +1
A new perturbation and continuation method is presented for computing and analyzing stellarator equilibria. The method is formally derived from a series expansion about the equilib…