An innovative adaptive kriging approach for efficient binary classification of mechanical problems
This work addresses efficient binary classification for mechanical problems like dynamics or damage, but it appears incremental as it builds on existing adaptive kriging methods.
The authors tackled the problem of binary classification for mechanical problems with fluctuating response surfaces by proposing a novel adaptive kriging scheme called MiVor, which achieved accurate classification with only a few observation points in such cases.
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.