Analysis of Computer Experiments Using Penalized Likelihood Gaussian Kriging Model
نویسندگان
چکیده
ABSTRACT Kriging is a popular analysis approach for computer experiment for the purpose of creating a cheap-to-compute "metamodel" as a surrogate to a computationally expensive engineering simulation model. The maximum likelihood approach is employed to estimate the parameters in the Kriging model. However, the likelihood function near the optimum may be flat in some situations, and this leads to the maximum likelihood estimate for the parameters in the covariance matrix to have a very large random variation. To overcome this difficulty, a penalized likelihood approach is proposed for the kriging model. The proposed method is particularly important in the context of a computationally intensive simulation model where the number of simulation runs must be kept small. We applied the proposed approach for the reduction of piston slap, an unwanted engine noise due to piston secondary motion. Issues related to practical implementation of the proposed approach are discussed.
منابع مشابه
Analysis of Computer Experiments Using Penalized Likelihood in Gaussian Kriging Models
Kriging is a popular analysis approach for computer experiments for the purpose of creating a cheapto-compute “meta-model” as a surrogate to a computationally expensive engineering simulation model. The maximum likelihood approach is used to estimate the parameters in the kriging model. However, the likelihood function near the optimum may be flat in some situations, which leads to maximum like...
متن کاملOn the Maximum Likelihood Training of Gradient-Enhanced Spatial Gaussian Processes
Abstract. Spatial Gaussian Processes, alias spatial linear models or Kriging estimators, are a powerful and wellestablished tool for the design and analysis of computer experiments in a multitude of engineering applications. A key challenge in constructing spatial Gaussian processes is the training of the predictor by numerically optimizing its associated maximum likelihood function depending o...
متن کاملIMPROVING GAUSSIAN MIXTURE DENSITY ESTIMATES 1 Averaging
We apply the idea of averaging ensembles of estimators to probability density estimation. In particular we use Gaussian mixture models which are important components in many neural network applications. One variant of averaging is Breiman's \bagging", which recently produced impressive results in classiication tasks. We investigate the performance of averaging using three data sets. For compari...
متن کاملTitle Penalized Blind Kriging in Computer Experiments Complete List of Authors Ying Hung Penalized Blind Kriging in Computer Experiments
Kriging models are popular in analyzing computer experiments. The most widely used kriging models apply a constant mean to capture the overall trend. This method can lead to a poor prediction especially when certain strong trends exist. To tackle this problem, a new modeling method is proposed, which incorporates a variable selection mechanism into kriging via a penalty function. An efficient a...
متن کاملA non-stationary space-time Gaussian Process model for partially converged simulations
In the context of expensive numerical experiments, a promising solution to alleviate the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at a price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data, for further use in prediction...
متن کامل