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SUMMARY:A proposal for multiresponse Kriging optimization
DTSTART:20220628T083000Z
DTEND:20220628T085000Z
DTSTAMP:20240224T220900Z
UID:indico-contribution-255@conferences.enbis.org
DESCRIPTION:Speakers: Nedka Dechkova Nikiforova (Department of Statistics
Computer Science Applications “G.Parenti”- University of Florence)\, R
ossella Berni\, Luciano Cantone (Department of Engineering for Enterprise
“Mario Lucertini”\, University of Rome “Tor Vergata”\, Rome\, Ital
y)\n\nPhysical experimentation for complex engineering and technological p
rocesses could be too costly\, or in certain cases\, impossible to be perf
ormed. Thus\, computer experiments are increasingly used in such context.
Specific surrogate models are adopted for the analysis of computer experim
ents which are statistical interpolators of the simulated input-output dat
a. Among such surrogate models\, a widely used one is the Kriging. The mai
n objective of Kriging modelling is the optimal prediction of the output (
i.e. the response variable) through a statistical model involving a determ
inistic part\, named trend function\, and a stochastic part\, namely a Gau
ssian random field with zero mean and stationary covariance function. In t
his talk\, we deal with a proposal for multiresponse Kriging optimization
with anisotropic covariance function. We consider the Universal Kriging mo
del which entails a non-constant trend function\, and allows to improve th
e accuracy of the estimated surface. The suggested optimization procedure
involves the definition of a single objective function which takes account
of the adjustment to the objective values for each response (i.e. targets
)\, the predicted Kriging mean and variance. In addition\, we consider tol
erance intervals for the targets\, rather than fixed values\, and weights
to take care of the different importance of each response variable. We app
ly our proposal to a case-study on freight trains reported in Nikiforova e
t al. (2021). The final results are currently in progress\, and further de
velopments will be also carried out by considering the choice of the covar
iance function\, and other suitable optimization measures. \nREFERENCES:\n
1) Nikiforova N. D.\, Berni R.\, Arcidiacono G.\, Cantone L. and Placidoli
P. (2021). Latin hypercube designs based on strong orthogonal arrays and
Kriging modelling to improve the payload distribution of trains. Journal o
f Applied Statistics\, 48 (3): 498-516\, DOI: 10.1080/02664763.2020.173394
3.\n\nhttps://conferences.enbis.org/event/18/contributions/255/
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URL:https://conferences.enbis.org/event/18/contributions/255/
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