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Conference Papers Year : 2023

Physics-aware modelling of an accelerated particle cloud

Abstract

Particle accelerator simulators, pivotal for acceleration optimization, are computationally heavy; surrogate, machine learning-based models are thus trained to facilitate the accelerator fine-tuning. While these current models are efficient, they do not allow for simulating the beam at the individual particle-level. This paper adapts point cloud deep learning methods, developed for computer vision, to model particle beams.
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Dates and versions

hal-04396175 , version 1 (16-01-2024)

Licence

Public Domain

Identifiers

  • HAL Id : hal-04396175 , version 1

Cite

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag. Physics-aware modelling of an accelerated particle cloud. MLPS 2023 - Machine Learning and the Physical Sciences Workshop 23023 - At the 37th conference on Neural Information Processing Systems (NeurIPS), Dec 2023, New Orleans, United States. ⟨hal-04396175⟩
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