Latin hypercube designs based on strong orthogonal arrays and Kriging modelling to improve the payload distribution of trains

DOI10.1080/02664763.2020.1733943OpenAlexW3008794445MaRDI QIDQ5861530

Gabriele Arcidiacono, Nedka Dechkova Nikiforova, Rossella Berni, Luciano Cantone, Pierpaolo Placidoli

Publication date: 1 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2020.1733943

zbMATH Keywords

computer experimentsfreight trainsanisotropic covariancein-train forcesKriging modellingpayload distribution

Mathematics Subject Classification ID

Design of statistical experiments (62K99) Applications of statistics in engineering and industry; control charts (62P30) Dynamical systems methods for problems in mechanics (70G60) Applications of statistics (62Pxx)

Related Items

Using nonregular designs to generate space-filling designs, Enhanced kriging leave-one-out cross-validation in improving model estimation and optimization

Uses Software

• DiceKriging
• DiceOptim
• FrF2

Cites Work

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