4.1. Introduction
This is a test: This part
This part 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. 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.
This ensemble (of size \(n_S\)) can be written as
where \(\mathbf{x}^i\) is the i-th input vector which can be written as \(\mathbf{x}^i=(x^i_1\, \ldots\, x^i_{n_X})\) and the output \(y^i = y(\mathbf{x}^i)\).
There are several predefined surrogate-models proposed in the Uranie platform:
The linear regression, discussed in The Linear Regression.
