Bayesian Optimization Algorithms for Accelerator Physics
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909162171006976 |
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| author | Roussel, Ryan Edelen, Auralee L. Boltz, Tobias Kennedy, Dylan Zhang, Zhe Ji, Fuhao Huang, Xiaobiao Ratner, Daniel Garcia, Andrea Santamaria Xu, Chenran Kaiser, Jan Pousa, Angel Ferran Eichler, Annika Lubsen, Jannis O. Isenberg, Natalie M. Gao, Yuan Kuklev, Nikita Martinez, Jose Mustapha, Brahim Kain, Verena Lin, Weijian Liuzzo, Simone Maria John, Jason St. Streeter, Matthew J. V. Lehe, Remi Neiswanger, Willie |
| author_facet | Roussel, Ryan Edelen, Auralee L. Boltz, Tobias Kennedy, Dylan Zhang, Zhe Ji, Fuhao Huang, Xiaobiao Ratner, Daniel Garcia, Andrea Santamaria Xu, Chenran Kaiser, Jan Pousa, Angel Ferran Eichler, Annika Lubsen, Jannis O. Isenberg, Natalie M. Gao, Yuan Kuklev, Nikita Martinez, Jose Mustapha, Brahim Kain, Verena Lin, Weijian Liuzzo, Simone Maria John, Jason St. Streeter, Matthew J. V. Lehe, Remi Neiswanger, Willie |
| contents | Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05667 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimization Algorithms for Accelerator Physics Roussel, Ryan Edelen, Auralee L. Boltz, Tobias Kennedy, Dylan Zhang, Zhe Ji, Fuhao Huang, Xiaobiao Ratner, Daniel Garcia, Andrea Santamaria Xu, Chenran Kaiser, Jan Pousa, Angel Ferran Eichler, Annika Lubsen, Jannis O. Isenberg, Natalie M. Gao, Yuan Kuklev, Nikita Martinez, Jose Mustapha, Brahim Kain, Verena Lin, Weijian Liuzzo, Simone Maria John, Jason St. Streeter, Matthew J. V. Lehe, Remi Neiswanger, Willie Accelerator Physics Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. |
| title | Bayesian Optimization Algorithms for Accelerator Physics |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2312.05667 |