Online storage ring optimization using dimension-reduction and genetic algorithms

Bergan, William; Bazarov, Ivan; Duncan, Cameron; Liarte, Danilo B.; Rubin, D.; Sethna, James P.

doi:10.1103/physrevaccelbeams.22.054601

Cited by 16 publications

(15 citation statements)

References 28 publications

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“…What is there left to do? In previous reviews and papers, we have explored and speculated about applications of information geometry to systems biology [71], power systems [23], robustness and neutral spaces in biology [1], controlling and optimizing complex instruments such as particle accelerators [72], and explaining why science works [7]. Here we focus on possible future developments related to the new tools and methods discussed here -understanding why hyperribbons and emergence arise, using their boundaries as simpler models, relations to emergence in physics, new Bayesian priors, and visualization methods that bypass the curse of dimensionality.…”

Section: Future Directionsmentioning

confidence: 99%

Information geometry for multiparameter models: New perspectives on the origin of simplicity

Quinn¹,

Abbott²,

Transtrum³

et al. 2021

Preprint

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Complex models in physics, biology, economics, and engineering are often ill-determined or sloppy: parameter combinations can vary over wide ranges without significant changes in their predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories. We introduce the model manifold of predictions, whose coordinates are the model parameters. Its hyperribbon structure explains why only a few parameter combinations matter for the behavior. We review recent rigorous results that connect the hierarchy of hyperribbon widths to approximation theory, and to the smoothness of model predictions under changes of the control variables. We discuss recent geodesic methods to find simpler models on nearby boundaries of the model manifold -emergent theories with fewer parameters that explain the behavior equally well. We discuss a Bayesian prior which optimizes the mutual information between model parameters and experimental data, naturally favoring points on the emergent boundary theories and thus simpler models. We introduce a 'projected maximum likelihood' prior that efficiently approximates this optimal prior, and contrast both to the poor behavior of the traditional Jeffreys prior. We discuss the way the renormalization group coarse-graining in statistical mechanics

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Section: Future Directionsmentioning

confidence: 99%

Information geometry for multiparameter models: New perspectives on the origin of simplicity

Quinn¹,

Abbott²,

Transtrum³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Optimization studies are important in the initial design of particle accelerator systems, when many trade-offs between possible setting combinations have to be explored. In practice, multiobjective optimization with genetic algorithms (GAs) [1,2] is frequently used for finding optimal setting combinations (see [3][4][5][6][7] for accelerator-specific examples). One advantage of using multiobjective optimization is that it enables one to examine optimal trade-offs between achievable beam parameters.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems

Edelen¹,

Neveu²,

Frey³

et al. 2020

Phys. Rev. Accel. Beams

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High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and experiment planning. It also precludes their use as on-line models tied directly to accelerator operation. We introduce an approach based on machine learning to create nonlinear, fastexecuting surrogate models that are informed by a sparse sampling of the physics simulation. The models are Oð10 6 Þ-Oð10 7 Þ times more computationally efficient to execute. We also demonstrate that these models can be reliably used with multiobjective optimization to obtain orders-of-magnitude speedup in initial design studies and experiment planning. For example, we required 132 times fewer simulation evaluations to obtain an equivalent solution for our main test case, and initial studies suggest that between 330-550 times fewer simulation evaluations are needed when using an iterative retraining process. Our approach enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.

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“…Realizing the design performance in spite of the many inevitable imperfections in the real machines is very challenging. In recent years, it has become a trend for accelerator physicists to resort to online optimization, i.e., directly optimizing the control parameters of the machines with computer algorithms during operation, to bring out the best machine performance and reduce tuning time ( Huang et al, 2013 ; Pang and Rybarcyk, 2014 ; Tian et al, 2014 ; Huang, 2016 ; Olsson, 2018 ; Bergan et al, 2019 ; Duris et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Teeport: Break the Wall Between the Optimization Algorithms and Problems

2021

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Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and the problems may be implemented in different languages, or they may require specific resources. We introduce an optimization platform named Teeport that is developed to address the above issues. This real-time communication-based platform is designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.

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Online storage ring optimization using dimension-reduction and genetic algorithms

Cited by 16 publications

References 28 publications

Information geometry for multiparameter models: New perspectives on the origin of simplicity

Information geometry for multiparameter models: New perspectives on the origin of simplicity

Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems

Teeport: Break the Wall Between the Optimization Algorithms and Problems

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