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Chapter III. The Sampler module

Chapter III. The Sampler module

Abstract

This is a description of the Sampler module whose main goal is to produce a design-of-experiments, starting from the description of the model provided by the user, and that would be used to investigate the sensitivity and/or propagate uncertainty for the requested analysis.
The source files are in souRCE.git/meTIER/sampler/souRCE and the corresponding namespace is URANIE::Sampler.

III.1. Introduction

The Sampler module is used to produce design-of-experiments knowing the expected behaviour of the input variables for the problem under consideration. The framework of our approach can be illustrated in the following schematic view:

Figure III.1. Schematic view of the input/output relation through a code

Schematic view of the input/output relation through a code


  • We will denote as the studied computational code which, generally, has two types of inputs:

    • The constant parameters which are gathered in the vector . They represent constants.

    • The uncertain parameters which are gathered in the vector

      It shall be noticed that these parameters are supposed to be uncertain either because of a lack of knowledge on their actual value or because of their intrinsic random nature.

  • The result of the code for a given set of parameters gives the vector which contains all the output variables of the analysis.

Different methods exist to obtain a design-of-experiments from uncertain parameters which can be classified into two categories:

  1. stochastic methods (see Section III.2). These methods consist in using a random number generator to produce new samples. This is also called Monte-Carlo.

  2. deterministic methods (see Section III.4). Two distinct calls with the same parameters will always give the same point in a design-of-experiments. Some of these methods (those discussed below) are sequences which are sometimes called quasi-Monte Carlo (qMC).

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