The Gaussian process as a computer model surrogate
Sparse Gaussian Process Emulators for surrogate design modelling
... In the authors opinion, it is not commonplace that the simulator is run at a large number of param- eter combinations. This problem mainly arises in a high dimensional parameter space where most of the parameters ...
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Validating Gaussian Process Models in Computer Experiments
... using computer experiments are many and ...low-order model of the Atlantic thermohaline circulation which is able to repro- duce many features of the behaviour of coupled ocean-atmosphere circulation models ...
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Gaussian process emulators for computer experiments with inequality constraints: Gaussian process emulators with inequality constraints
... new model for incorporating both interpolation con- ditions and inequality constraints into a Gaussian process ...of Gaussian processes which converges uniformly ...and Gaussian random ...
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Multi-fidelity Gaussian process regression for computer experiments
... Figure 5.5 illustrates the efficiency of the criterion AdjMMSE. Indeed, for the Shubert’s and Michalewicz’s functions, we see that the accuracy of the 1 point at-a-time kriging with this criterion is significantly better ...
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The Gaussian Process Autoregressive Regression Model (GPAR)
... Tidal height, wind speed, and air temperature data set. 6 This data set was collected at 5 minute in- tervals by four weather stations: Bramblemet, Camber- met, Chimet, and Sotonmet, all located in Southamp- ton, UK. The ...
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Gaussian Process Model Based Predictive Control
... INTRODUCTION Model Predictive Control (MPC) is a common name for computer control algorithms that use an explicit process model to predict the future plant ...Linear model based ...
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Model selection with application to gamma process and inverse Gaussian process
... gamma process and the inverse Gaussian process are suitable for modeling gradual damage in- troduced by continuous ...“wrong” model is probably larger than that of the “right” ...wrong ...
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Model selection with application to gamma process and inverse Gaussian process
... gamma process and the inverse Gaussian process are widely used in condition-based main- ...gamma process can be well approximated by an inverse Gaussian process or the other way ...
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A gaussian process latent variable model for BRDF inference
... Application 2 - Flash-based photography. As a final experiment, we measured the BRDF of a real object with complex geometry. Fig. 7 shows a Buddha statue, the shape of which is extracted using Structure-from-Motion. ...
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Over-Fitting in Model Selection with Gaussian Process Regression
... Abstract. Model selection in Gaussian Process Regression (GPR) seeks to determine the optimal values of the hyper-parameters governing the covariance function, which allows flexible customization of ...
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A gaussian process based multi-person interaction model
... In this paper, we propose a new predictive model for a recursive Bayesian filter on the basis of Gaussian Process Regression. Us- ing the proposed method, the state vector of a pedestrian is pre- ...
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A Gaussian Process Regression Model for the Traveling Salesman Problem
... method, Gaussian Process Regression (GPR) and the iterated local search is proposed to solve a deterministic symmetric TSP with a single ...a Gaussian process regression ...
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Discriminative Gaussian Process Latent Variable Model for Classification
... sian Process Latent Variable Models can discover low dimensional manifolds given only a small number of examples, but learn a latent space without regard for class ...for Gaussian Process ...
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Adding Flight Mechanics to Flight Loads Surrogate Model using Multi-Output Gaussian Processes
... knowledge of relation between the outputs a joint family of functions can be constructed. Later functions inconsistent with the observed data are eliminated using bayes rule. This gives us a prediction which is ...
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On the Bayesian treed multivariate Gaussian process with linear model of coregionalization.
... can model only a special case of non-stationarity since it does not allow for the spatial correlation to vary on ...multivariate model based on the Bayesian treed multivariate Gaussian process ...
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Error-Controlled Model Approximation for Gaussian Process Morphable Models
... The Gaussian Process Morphable Model (GPMM) frame- work [26], on which our work is based on, can be seen as the unification of different ...initial model needs to be of finite rank ...
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Gaussian Process Model Predictive Control of Unknown Nonlinear Systems
... Abstract: Model Predictive Control (MPC) of an unknown system that is modelled by Gaussian Process (GP) techniques is studied in this ...take model uncertainty into ...Stochastic Model ...
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A comparison of polynomial chaos and Gaussian process emulation for uncertainty quantification in computer experiments
... in computer experiments is an important and rapidly expanding field, applying to all areas of science which use simulators to conduct experiments about a physical phenomenon of ...the computer simulation ...
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Gaussian processes for computer experiments
... (computer model output) are known to satisfy linear inequality constraints (such as boundedness, monotonicity and convexity) with respect to some or all input ...In computer experiment framework, ...
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Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
... experts model that distributes the computational load amongst a large set of independent computational ...Our model recursively recombines computations by these independent units to an overall GP ...
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