Review on Python Toolboxes for Kriging Surrogate Modelling

Abstract

Despite the computing power achieved with current technologies, computationally intensive analyses (e.g. optimization problems, reliability or sensitivity analyses) actually remain a time-consuming problem in modern engineering. Traditionally, a widely used approach to deal with this issue based on learning from data, relies on metamodels or surrogate modeling. As a result, the emulator has the attraction of being fast to evaluate. In this context, metamodels based on Kriging (a.k.a. Gaussian Process (GP)) have gained momentum in computational sciences by playing a crucial role in the expansion of machine learning tools. The specific objective of this study is to investigate the use of different Python libraries for Kriging metamodeling purposes, setting out a consistently review of the major frameworks used in the engineering field. In particular, a focus on two primary aims is addressed: (a) to compare the various settings available for each library; (b) to ascertain how they perform and differ under similar assumptions. In this investigation, the aim is to be of value to practitioners wanting to know on the capability and reliability of the multitude of open-source packages available nowadays.