Computer experiments: a review

Levy, Sigal; Steinberg, David M.

doi:10.1007/s10182-010-0147-9

Cited by 91 publications

(50 citation statements)

References 74 publications

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“…We use a small subset of these points ≈ 8% to test the GPE procedure. The algorithm works as follow (i) Random points in the configuration space are selected using a Latin Hypercube Sampling (LHS) [57]. This is because the GPE performances are much better if it starts from random points rather than from a uniform grid.…”

Section: B Gaussian Process Emulatormentioning

confidence: 99%

A new statistical method for the structure of the inner crust of neutron stars

Pastore¹,

Shelley²,

Baroni³

et al. 2017

J. Phys. G: Nucl. Part. Phys.

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We investigated the structure of the low density regions of the inner crust of neutron stars using the Hartree-Fock-Bogoliubov (HFB) model to predict the proton content Z of the nuclear clusters and, together with the lattice spacing, the proton content of the crust as a function of the total baryonic density ρ b . The exploration of the energy surface in the (Z, ρ b ) configuration space and the search for the local minima require thousands of calculations. Each of them implies an HFB calculation in a box with a large number of particles, thus making the whole process very demanding. In this work, we apply a statistical model based on a Gaussian Process Emulator that makes the exploration of the energy surface ten times faster. We also present a novel treatment of the HFB equations that leads to an uncertainty on the total energy of ≈ 4 keV per particle. Such a high precision is necessary to distinguish neighbour configurations around the energy minima.

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Section: B Gaussian Process Emulatormentioning

confidence: 99%

A new statistical method for the structure of the inner crust of neutron stars

Pastore¹,

Shelley²,

Baroni³

et al. 2017

J. Phys. G: Nucl. Part. Phys.

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“…Hence, many landscape analysis techniques are based on a sample of the solutions and/or their respective fitness values. Common sampling methodologies include sampling from an assumed distribution (typically uniform and independently and identically distributed over variables), random walks, Latin hypercube design, space filling techniques, and algorithm trajectories [1], [2]. Choosing an appropriate sampling strategy is nontrivial, and as a result it is often motivated by the objectives and/or requirements of the problem analysis technique.…”

Section: Sampling Methodologies and Distance Metricsmentioning

confidence: 99%

Sampling Techniques and Distance Metrics in High Dimensional Continuous Landscape Analysis: Limitations and Improvements

Morgan

Gallagher

2014

IEEE Trans. Evol. Computat.

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Abstract-In metaheuristic optimization, understanding the relationship between problems and algorithms is important but nontrivial. There has been a growing interest in the literature on techniques for analyzing problems and algorithm performance; however, the validity of the assumptions and implementation choices behind many techniques is often not closely examined. In this paper, we review some interesting theoretical properties regarding sampling techniques and distance metrics in continuous spaces. In particular, we examine the effect of using Euclidean distance in conjunction with uniform random sampling on the behavior of the Dispersion metric. We show that the current methodology employed for the estimation of dispersion has important flaws, and we propose and evaluate modifications to improve the methodology. The modifications are simple and do not add significant complexity or computational effort to the methodology.

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“…The first paper by Levy and Steinberg (2010) gives a general review of computer experiments. After identifying a wide range of applications, they describe the popular Gaussian process model, as well as possible alternatives and extensions.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of computer experiments

Kuhnt

Steinberg

2010

AStA Adv Stat Anal

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The design and analysis of computer experiments as a relatively young research field is not only of high importance for many industrial areas but also presents new challenges and open questions for statisticians. This editorial introduces a special issue devoted to the topic. The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications.

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Computer experiments: a review

Abstract: Gaussian process, Latin hypercube designs, Deterministic output, Functional data, Space filling,

Cited by 91 publications

References 74 publications

A new statistical method for the structure of the inner crust of neutron stars

A new statistical method for the structure of the inner crust of neutron stars

Sampling Techniques and Distance Metrics in High Dimensional Continuous Landscape Analysis: Limitations and Improvements

Design and analysis of computer experiments

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