Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization

• URL: http://arxiv.org/abs/2203.13968v2
• Date: Tue, 29 Mar 2022 13:05:18 GMT
• Title: Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
• Authors: Johannes Kirschner, Mojmir Mutn\'y, Andreas Krause, Jaime Coello de Portugal, Nicole Hiller, Jochem Snuverink
• Abstract summary: tuning machine parameters of particle accelerators is a repetitive and time-consuming task. We propose and evaluate a step size-limited variant of safe Bayesian optimization.
• Score: 73.94660141019764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tuning machine parameters of particle accelerators is a repetitive and time-consuming task, that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited because most methods do not account for safety-critical constraints that apply to each iteration, including loss signals or step-size limitations. One notable exception is safe Bayesian optimization, which is a data-driven tuning approach for global optimization with noisy feedback. We propose and evaluate a step size-limited variant of safe Bayesian optimization on two research faculties of the Paul Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL) and b) the High-Intensity Proton Accelerator (HIPA). We report promising experimental results on both machines, tuning up to 16 parameters subject to more than 200 constraints.

Related papers

• Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization [0.0]
  We propose satisficing Thompson sampling-based parallel BO approaches, including synchronous and asynchronous versions. We shift the target from an optimal solution to a satisficing solution that is easier to learn. The effectiveness of the proposed methods is demonstrated on a fast-charging design problem of Lithium-ion batteries.
  arXiv  Detail & Related papers  (2023-10-19T07:03:51Z)
• Accelerated First-Order Optimization under Nonlinear Constraints [73.2273449996098]
  We exploit between first-order algorithms for constrained optimization and non-smooth systems to design a new class of accelerated first-order algorithms. An important property of these algorithms is that constraints are expressed in terms of velocities instead of sparse variables.
  arXiv  Detail & Related papers  (2023-02-01T08:50:48Z)
• Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders [28.550684606186884]
  We develop a new approach which jointly trains a denoising autoencoder with a discriminative multi-task Gaussian process head. We evaluate LaMBO on a small-molecule based on the ZINC dataset and introduce a new large-molecule task targeting fluorescent proteins.
  arXiv  Detail & Related papers  (2022-03-23T21:58:45Z)
• Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding [48.7576911714538]
  Finding the optimal process parameters for an adhesive bonding process is challenging. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem. In this research, we successfully applied specific machine learning techniques to emulate the objective and constraint functions.
  arXiv  Detail & Related papers  (2021-12-16T10:14:39Z)
• Two-step Lookahead Bayesian Optimization with Inequality Constraints [21.703234193908038]
  We propose a two-step lookahead constrained Bayesian optimization acquisition function (2-OPT-C) supporting both sequential and batch settings. In numerical experiments, 2-OPT-C typically improves query efficiency by 2x or more over previous methods, and in some cases by 10x or more.
  arXiv  Detail & Related papers  (2021-12-06T07:40:54Z)
• Optimizing quantum control pulses with complex constraints and few variables through Tensorflow [0.0]
  We show how to apply optimal control algorithms on realistic quantum systems by incorporating multiple constraints into the gradient optimization. We test our algorithm by finding smooth control pulses to implement single-qubit and two-qubit gates for superconducting transmon qubits with always-on interaction. Our algorithm provides a promising optimal quantum control approach that is friendly to complex and optional physical constraints.
  arXiv  Detail & Related papers  (2021-10-11T14:59:28Z)
• An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemble [11.64233949999656]
  We propose an efficient parallelizable Bayesian optimization algorithm via Multi-objective ACquisition function Ensemble (MACE) Our proposed algorithm can reduce the overall simulation time by up to 74 times compared to differential evolution (DE) for the unconstrained optimization problem when the batch size is 15. For the constrained optimization problem, our proposed algorithm can speed up the optimization process by up to 15 times compared to the weighted expected improvement based Bayesian optimization (WEIBO) approach, when the batch size is 15.
  arXiv  Detail & Related papers  (2021-06-28T13:21:28Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.