We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cyclic parameters of the optimization method to get a balance between local and global...

In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original ...

We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cyclic parameters of the optimization method to get a balance between local and global...

The surrogate data methodology is used to test a given time series for membership of specific classes of dynamical systems. Currently, there are three algorithms that are widely applied in the literature. The most general of these tests the hypothesis of nonlinearly scaled linearly filtered noise. However, these tests and the many extensions of them that have been suggested are inappropriate fo...

This paper describes an accurate and computationally efficient surrogate method, known as the polynomial dimensional decomposition (PDD) method, for estimating a general class of density-based fsensitivity indices. Unlike the variance-based Sobol index, the f-sensitivity index is applicable to random input following dependent as well as independent probability distributions. The proposed method...

Article history: Received 31 December 2010 Received in revised form 13 July 2011 Accepted 11 August 2011 Available online 28 August 2011

In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter y. The performance parameter y is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of y. We propose to use the multicanonical Monte Carlo (MMC) meth...

In the Surrogate Method, the measured decay probability of a compound nucleus formed via a direct reaction is used to extract the cross section for a reaction with a different entrance channel that proceeds through the same compound nucleus. An extension of the Surrogate Method, the Surrogate Ratio Method (SRM), uses a ratio of measured decay probabilities to infer an unknown cross section rela...

Relative 235 U(n,γ) and (n,f) cross sections from 235 U(d,pγ) and (d,pf) The internal surrogate ratio method allows for the determination of an unknown cross section, such as (n,γ), relative to a better-known cross section, such as (n,f), by measuring the relative exit-channel probabilities of a surrogate reaction that proceeds through the same compound nucleus. The validity of the internal sur...

The paper deals with surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed. Such an extension ...