English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Large computer codes are widely used in engineering to study physicalsystems. Nevertheless, simulations can sometimes be time-consuming. In thiscase, an approximation of the code input/output relation is made using ametamodel. Actually, a computer code can often be run at different levels ofcomplexity and a hierarchy of levels of code can hence be obtained. Forexample, it can be a finite element model with a more or less fine mesh. Theaim of our research is to study the use of several levels of a code to predictthe output of a costly computer code. The presented multi-stage metamodel is aparticular case of co-kriging which is a well-known geostatistical method. Wefirst describe the construction of the co-kriging model and we focus then on asequential experimental design strategy. Indeed, one of the strengths ofco-kriging is that it provides through the predictive co-kriging variance anestimation of the model error at each point of the input space parameter.Therefore, to improve the surrogate model we can sequentially add points in thetraining set at locations where the predictive variances are the largest ones.Nonetheless, in a multi-fidelity framework, we also have to choose which levelof code we have to run. We present here different strategies to choose thislevel. They are based on an original result which gives the contribution ofeach code on the co-kriging variance.

Planification d'exp\'eriences s\'equentielle dans un contexte de m\'eta-mod\'elisation multi-fid\'elit\'e