2017
DOI: 10.1117/12.2270609
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Global optimization of complex optical structures using Bayesian optimization based on Gaussian processes

Abstract: Numerical simulation of complex optical structures enables their optimization with respect to specific objectives. Often, optimization is done by multiple successive parameter scans, which are time consuming and computationally expensive. We employ here Bayesian optimization with Gaussian processes in order to automatize and speed up the optimization process. As a toy example, we demonstrate optimization of the shape of a free-form reflective meta surface such that it diffracts light into a specific diffractio… Show more

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Cited by 17 publications
(14 citation statements)
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“…Based on the previous observation of the objective the algorithm identifies parameter values where it is expected to find a smaller function value. 11,21 Gaussian processes. Gaussian processes (GP) are frequently used as the stochastic model in Bayesian optimization.…”
Section: Bayesian Optimization Methodsmentioning
confidence: 99%
“…Based on the previous observation of the objective the algorithm identifies parameter values where it is expected to find a smaller function value. 11,21 Gaussian processes. Gaussian processes (GP) are frequently used as the stochastic model in Bayesian optimization.…”
Section: Bayesian Optimization Methodsmentioning
confidence: 99%
“…The Bayesian optimization method uses the statistical information provided by the posterior GP to determine promising parameter sets and to sequentially converge to the optimum value of f ob . There are different strategies for how to use the information provided by the GP [20]. One commonly used strategy, which is also used in this work, consists in finding the point of maximum expected improvement [17].…”
Section: B Bayesian Optimization With Gaussian Processesmentioning
confidence: 99%
“…There are two main steps during which Bayesian optimization suffers from scalability issues: the inversion of the covariance matrix and the evaluation of the acquisition function [20] at different points. In a previous work [19], we proposed a technique to mitigate the problem of the time for evaluating the acquisition function.…”
Section: Introductionmentioning
confidence: 99%
“…Bayesian Optimization (BO) is a procedure based on a stochastic model given by Gaussian processes (GPs) [21]. The optimization starts at a random point in the parameter space and predicts the objective function in the full space based on the previously obtained values.…”
Section: A9 Bayesian Optimizationmentioning
confidence: 99%