Reinforcement learning agent trained in DIII-D tokamak simulator achieves 2.01 cm mean shape error on held-out data, tracks dynamic targets, and remains functional under 30% random sensor dropout with direct transfer to experimental shots.
Remote Sensing of Environment245, 111797 (2020)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.
citing papers explorer
-
Dynamic Plasma Shape Control with Arbitrary Sensor Subsets
Reinforcement learning agent trained in DIII-D tokamak simulator achieves 2.01 cm mean shape error on held-out data, tracks dynamic targets, and remains functional under 30% random sensor dropout with direct transfer to experimental shots.
-
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.