A new comprehensive lattice description schema and start-to-end simulation framework for particle accelerators that enables seamless use across multiple simulation codes.
Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A framework for simultaneous model predictive control and online parameter estimation is introduced by treating differentiable physics simulators as computational objects for gradient-based joint optimization.
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
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A flexible start-to-end simulation framework for particle accelerators based on a comprehensive lattice description
A new comprehensive lattice description schema and start-to-end simulation framework for particle accelerators that enables seamless use across multiple simulation codes.
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MPC and System Identification with Differentiable Physics: Fluid System and Particle Beam Control
A framework for simultaneous model predictive control and online parameter estimation is introduced by treating differentiable physics simulators as computational objects for gradient-based joint optimization.
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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.