An innovative adaptive kriging approach for efficient binary classification of mechanical problems

TL;DR

This paper introduces a novel adaptive kriging method called MiVor for efficient binary classification in mechanical problems, especially effective for complex, fluctuating response surfaces, outperforming existing schemes with fewer samples.

Contribution

The paper presents the MiVor adaptive scheme, a new approach that improves binary classification accuracy in mechanical problems using kriging with minimal samples.

Findings

MiVor performs well on analytical and mechanical problems.

It is especially effective for highly fluctuating response surfaces.

MiVor requires fewer observations for accurate classification.

Abstract

Kriging is an efficient machine-learning tool, which allows to obtain an approximate response of an investigated phenomenon on the whole parametric space. Adaptive schemes provide a the ability to guide the experiment yielding new sample point positions to enrich the metamodel. Herein a novel adaptive scheme called Monte Carlo-intersite Voronoi (MiVor) is proposed to efficiently identify binary decision regions on the basis of a regression surrogate model. The performance of the innovative approach is tested for analytical functions as well as some mechanical problems and is furthermore compared to two regression-based adaptive schemes. For smooth problems, all three methods have comparable performances. For highly fluctuating response surface as encountered e.g. for dynamics or damage problems, the innovative MiVor algorithm performs very well and provides accurate binary classification with only a few observation points.