Authors: Ryan T. Sandberg, Remi Lehe, Chad E. Mitchell, Marco Garten, Andrew Myers, Ji Qiang, Jean-Luc Vay, Axel Huebl

Published on: February 27, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2402.17248

Summary

• What is new: Implementation and presentation of a data-driven workflow using machine learning for surrogate modeling in particle accelerator simulations.
• Why this is important: Need for integrating and modeling advanced accelerator elements quickly and effectively without relying solely on traditional analytical and reduced-physics models.
• What the research proposes: A fully GPU-accelerated, conventional-surrogate simulation for hybrid particle accelerator beamlines using machine learning for surrogate modeling.
• Results: The paper does not specify the results explicitly, implying that a detailed benchmarking of the workflow was conducted to showcase its effectiveness.

Technical Details

Technological frameworks used: GPU-accelerated computation, machine learning for surrogate modeling

Models used: Particle-in-cell simulations, conventional-surrogate simulation models

Data used: High-fidelity simulations data

Potential Impact

Scientific research institutions and companies involved in the development and operation of particle accelerators; potentially impacting sectors reliant on particle accelerator technologies, such as medical imaging, cancer treatment, materials science, and nuclear physics.

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