2 citations · 3 across the 3 of their papers we have counts for
3 papers
physics.plasm-ph2024★ 1 cited
Combing physics-based and data-driven predictions for quantitatively accurate models that extrapolate well; with application to DIII-D, AUG, and ITER tokamak fusion reactors
Joseph Abbate, Emiliano Fable, Giovanni Tardini +3
Methodologies for combining the accuracy of data-driven models with extrapolability of physics-based models are described and tested, for the task of building transport models of t…
physics.plasm-ph2024★ 2 cited
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…
cs.LG2022
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…