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2009.03566
Cited By

Physics-informed Gaussian Process for Online Optimization of Particle
Accelerators

Physics-informed Gaussian Process for Online Optimization of Particle Accelerators

Physical Review Accelerators and Beams (PRAB), 2020

8 September 2020

ArXiv (abs)PDF HTML

Papers citing "Physics-informed Gaussian Process for Online Optimization of Particle Accelerators"

5 / 5 papers shown

Physics-enhanced Gaussian Process Variational Autoencoder

Physics-enhanced Gaussian Process Variational AutoencoderConference on Learning for Dynamics & Control (L4DC), 2023

George J. Pappas

253

5

0

15 May 2023

Multipoint-BAX: A New Approach for Efficiently Tuning Particle
Accelerator Emittance via Virtual Objectives

Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives

Sara Ayoub Miskovich

Willie Neiswanger

Jacqueline Garrahan

382

4

0

10 Sep 2022

Tuning Particle Accelerators with Safety Constraints using Bayesian
Optimization

Tuning Particle Accelerators with Safety Constraints using Bayesian OptimizationPhysical Review Accelerators and Beams (PRAB), 2022

Johannes Kirschner

J. C. D. Portugal

323

17

0

26 Mar 2022

Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a
Free-Electron Laser

Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron LaserFrontiers of Physics (Front. Phys.), 2022

146

5

0

15 Jan 2022

Model-free and Bayesian Ensembling Model-based Deep Reinforcement
Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

Simon Hirlaender

202

28

0

17 Dec 2020

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