5.1. Introduction

This is a test: The Modeler module

The Modeler module discusses the generation of surrogate models which aim to provide a simpler, and hence faster, model in order to emulate the specified output of a more complex model (and generally time and memory consuming) as a function of its inputs and parameters, provided through a TDataServer. The input dataset can either be an existing set of elements (provided by someone else, resulting from simulations or experiments) or it can be a design of experiments generated on purpose, for the sake of the ongoing study.

The meta-model generation is encoded in Uranie with several different kinds of surrogate models, and also different kinds of possible output format. Once created, the resulting model can indeed be transmitted to another code and re-used within or without Uranie, in order to avoid regeneration but also to keep track of achieved performances, as these models can sometimes be created based on a certain randomness (as discussed in the few sections below).

There are several predefined surrogate-models proposed in the Uranie platform: