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

This paper proposes a sequential design for maximizing a stochastic computersimulator output, y(x), over an unknown optimization domain. The training dataused to estimate the optimization domain are a set of (historical) inputs,often from a physical system modeled by the simulator. Two methods are providedfor estimating the simulator input domain. An extension of the well-knownefficient global optimization algorithm is presented to maximize y(x). Thedomain estimation/maximization procedure is applied to two readily understoodanalytic examples. It is also used to solve a problem in nuclear safety bymaximizing the k-effective "criticality coefficient" of spent fuel rods,considered as one-dimensional heterogeneous fissile media. One of the twodomain estimation methods relies on expertise-type constraints. We show thatthese constraints, initially chosen to address the spent fuel rod example, arerobust in that they also lead to good results in the second analyticoptimization example. Of course, in other applications, it could be necessaryto design alternative constraints that are more suitable for theseapplications.

Sequential Design of Mixture Experiments with an Empirically Determined  Input Domain and an Application to Burn-up Credit Penalization of Nuclear  Fuel Rods