Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments

Zhang, Ru; Lin, Cong; Ranjan, Pritam

doi:10.1080/10618600.2018.1473778

Cited by 12 publications

(29 citation statements)

References 34 publications

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“…A natural variation may be to allow elements of the local path (or set) to be treated differentially, say by weighting. Such an extension may prove useful in modeling dynamic computer simulations via local GPs (Zhang et al, 2016), replacing hard clusterings with weights, say. Another would be to augment the criterion defined in Eq.…”

Section: Discussionmentioning

confidence: 99%

Emulating Satellite Drag from Large Simulation Experiments

Sun¹,

Gramacy²,

Haaland³

et al. 2019

SIAM/ASA J. Uncertainty Quantification

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Obtaining accurate estimates of satellite drag coefficients in low Earth orbit is a crucial component in positioning and collision avoidance. Simulators can produce accurate estimates, but their computational expense is much too large for real-time application. A pilot study by Mehta et al. (2014b) showed that Gaussian process (GP) surrogate models could accurately emulate simulations. However, cubic runtime for training GPs means that they could only be applied to a narrow range of input configurations to achieve the desired level of accuracy. In this paper we show how extensions to the local approximate GP (laGP) method allow accurate full-scale emulation. The new methodological contributions, which involve a multilevel global/local modeling approach, and a setwise approach to local subset selection, are shown to perform well in benchmark and synthetic data settings. We conclude by demonstrating that our method achieves the desired level of accuracy, besting simpler viable (i.e., computationally tractable) global and local modeling approaches, when trained on seventy thousand core hours of drag simulations for two real-world satellites: the Hubble space telescope (HST) and the gravity recovery and climate experiment (GRACE).

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Section: Discussionmentioning

confidence: 99%

Emulating Satellite Drag from Large Simulation Experiments

Sun¹,

Gramacy²,

Haaland³

et al. 2019

SIAM/ASA J. Uncertainty Quantification

View full text Add to dashboard Cite

show abstract

“…Of course, the resultant expected improvement criteria would change, and in fact, one may not even end up with a closed form expression of the final design criterion for selecting follow-up points. Future work also include the application of the proposed contour estimation-based sequential design approaches for global fitting for computer experiments with both qualitative and quantitative factors (Deng et al, 2017) and dynamic computer experiments (Zhang, Lin and Ranjan, 2018).…”

Section: Concluding Remarkmentioning

confidence: 99%

Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator

Yang

Lin

Ranjan

2019

J Stat Theory Pract

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Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected. Extra care should be taken in the selection process because computer simulators can be computationally expensive to run. The selection shall acknowledge and achieve the goal of the analysis. This article focuses on the goal of producing more accurate prediction which is important for risk assessment and decision making. We propose two new methods of design approaches that sequentially select input settings to achieve this goal. The approaches make novel applications of simultaneous and sequential contour estimations. Numerical examples are employed to demonstrate the effectiveness of the proposed approaches.

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“…In related literature pertaining to computer experiments, localized approximations of Gaussian process models are proposed, see for e.g. Gramacy and Apley (2015), Zhang et al (2016) and Park and Apley (2017). This literature is overwhelmingly frequentist, less model based and has different goals compared to the Bayesian spatial literature.…”

Section: Introductionmentioning

confidence: 99%

Large Multi-scale Spatial Modeling Using Tree Shrinkage Priors

Guhaniyogi¹,

Sansó²

2020

STAT SINICA

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We develop a multiscale spatial kernel convolution technique with higher order functions to capture fine scale local features and lower order terms to capture large scale features. To achieve parsimony, the coefficients in the multiscale kernel convolution model is assigned a new class of "Tree shrinkage prior" distributions. Tree shrinkage priors exert increasing shrinkage on the coefficients as resolution grows so as to adapt to the necessary degree of resolution at any sub-domain. Our proposed model has a number of significant features over the existing multi-scale spatial models for big data. In contrast to the existing multiscale approaches, the proposed approach autotunes the degree of resolution necessary to model a subregion in the domain, achieves scalability by suitable parallelization of local updating of parameters and is buttressed by theoretical support. Excellent empirical performances are illustrated using several simulation experiments and a geostatistical analysis of the sea surface temperature data from the pacific ocean.

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Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments

Cited by 12 publications

References 34 publications

Emulating Satellite Drag from Large Simulation Experiments

Emulating Satellite Drag from Large Simulation Experiments

Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator

Large Multi-scale Spatial Modeling Using Tree Shrinkage Priors

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