Optimization and sensitivity analysis of computer simulation models by the score function method

Abstract

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A new sampling-based RBDO method via score function with reweighting scheme and application to vehicle designs

“…The commonly used RBDO methods can be classified into two categories: one is the most probable point (MPP)-based methods such as first-order reliability method (FORM), second-order reliability method (SORM), reliability index approach (RIA) and performance measure approach (PMA) etc., where the accurate sensitivities of performance http://dx.doi.org/10.1016/j.apm.2014. 11.045 0307-904X/Ó 2014 Elsevier Inc. All rights reserved. function are required in complex engineering systems.…”

“…The score function method is proposed to address those issues when applying FDM to calculate the sensitivities of probabilistic responses [11,12]. It uses the information of probability density function (PDF) for obtaining the derivatives of the probabilistic response analytically by using MCS.…”

Universal properties of kernel functions for probabilistic sensitivity analysis

“…doi: 10 integral of moments of normal kernel functions two probability density functions of the performance response, obtained before and after the variance removal of a random variable. The application is similar to the variance-based methods but is not limited to differences in the second moment.…”

“…Kleijnen and Rubinstein [10], Rubinstein [11], and Rubinstein and Shapiro [12] discuss the "score function" method for the computation of partial derivatives of a performance function with respect to parameters of the underlying probability distributions. The method is applied to discrete event simulations.…”

Monte Carlo Techniques for Parametric Finite Multidimensional Integral Equations

“…The most straightforward method is the Score Function (SF). The SF approach [24,32,33] is the major method for estimating the performance measure and its derivative, while observing only a single sample path from the underlying system [32]. The basic idea of SF is that the derivative of the performance function, J (v), is expressed as expectation with respect to the same distribution as the performance measure itself.…”