Targeted halting of gradient flow at unstable material boundaries enables stable derivatives for optimizing detector designs in radiation transport simulations.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
RL-ABC is a framework that formulates accelerator beamline tuning as a Markov decision process with a 57-dimensional state and configurable reward, enabling a DDPG agent to reach 70.3% particle transmission on a VEPP-5 test beamline, matching differential evolution.
citing papers explorer
-
Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Targeted halting of gradient flow at unstable material boundaries enables stable derivatives for optimizing detector designs in radiation transport simulations.
-
RL-ABC: Reinforcement Learning for Accelerator Beamline Control
RL-ABC is a framework that formulates accelerator beamline tuning as a Markov decision process with a 57-dimensional state and configurable reward, enabling a DDPG agent to reach 70.3% particle transmission on a VEPP-5 test beamline, matching differential evolution.