A Tutorial on Latin Hypercube Design of Experiments

Viana, Felipe A. C.

doi:10.1002/qre.1924

Cited by 159 publications

(55 citation statements)

References 78 publications

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“…In or-der to reach the stated goal, a new Bayesian active learning approach is proposed, which is summarized in Figure 1. The first step is to evaluate via CAD simulations the objective function(s) of interest for a small set of initial samples in the design space, which are chosen via Latin hypercube design (LHD) [5]. Based on the initial simulations, a regression model of each objective function is built.…”

Section: Bayesian Active Learning Frameworkmentioning

confidence: 99%

Bayesian active learning for multi‐objective feasible region identification in microwave devices

Garbuglia

Qing

Knudde

et al. 2021

Electronics Letters

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In microwave device and circuit design, many simulations are often needed to find a set of designs that satisfy one or multiple specifications chosen by the designer upfront: the feasible region. A novel Bayesian active learning framework is presented to accurately identify the feasible region with a low number of simulations. The technique leverages on a stochastic model to obtain an efficient and automated procedure. A suitable application example validates the proposed technique and shows its effectiveness to rapidly obtain many suitable designs.

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Section: Bayesian Active Learning Frameworkmentioning

confidence: 99%

Bayesian active learning for multi‐objective feasible region identification in microwave devices

Garbuglia

Qing

Knudde

et al. 2021

Electronics Letters

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“…Inspired by [ 82 ] in the case of non-independent multivariate input variables, the desired correlation matrix can be used to produce distribution-free sample points in LHS. For more information, refer to [ 39 , 83 ].…”

Section: Proposed Algorithmmentioning

confidence: 99%

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

et al. 2020

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This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to the changeability of a target of the control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.

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“…This technique has been developed based on space‐filling properties by producing random points. For more information on LHS technique, refer to References 60 and 61.…”

Section: Surrogate‐based Robust Simulation‐optimization (Two‐layer Sumentioning

confidence: 99%

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Parnianifard

Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

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This paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

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A Tutorial on Latin Hypercube Design of Experiments

Cited by 159 publications

References 78 publications

Bayesian active learning for multi‐objective feasible region identification in microwave devices

Bayesian active learning for multi‐objective feasible region identification in microwave devices

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

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