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Data-driven Kriging models based on FANOVA-decomposition
2011
Statistics and computing
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This is achieved by exploring the interaction structure of the output based on FANOVA methods. ...
As a solution a modified Kriging model is proposed, which reflects the interaction structure inherent to the data generating mechanism. ...
Constructing a Kriging model with a correlation structure according this cliques structure, the prediction error can be calculated based on the data set of size 12. ...
doi:10.1007/s11222-011-9259-7
fatcat:l5b52fjqyjglretmhtvurgumz4
Modeling of computer experiments for uncertainty propagation and sensitivity analysis
2011
Statistics and computing
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While computer simulations are faster and cheaper than physical experiments, computer models generate data (often large amounts) that must be analyzed and care is needed at the design stage to determine ...
This short list of problems is, of course, not exhaustive but one can easily understand that proper statistical and A. Antoniadis ( ) Lab. ...
Mühlenstädt et al. propose a data-driven methodology to build effective kriging emulators, the covariance of which explicitly takes into account the interaction structure of the data. ...
doi:10.1007/s11222-011-9282-8
fatcat:c5ytysyzunaa7hioq262vm3zzi
Regression and Kriging metamodels with their experimental designs in simulation: A review
2017
European Journal of Operational Research
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It focusses on analysis via either low-order polynomial regression or Kriging (also known as Gaussian process) metamodels. ...
Optimization of the simulated system may use either a sequence of low-order polynomials known as response surface methodology (RSM) or Kriging models ...tted through sequential designs including e¢ cient ...
the following two approaches: (i) Fit one Kriging model for E(wjd) and one Kriging model for (wjd)-estimating both models from the same simulation I/O data. ...
doi:10.1016/j.ejor.2016.06.041
fatcat:7wzanl3xmbhnpbisre2v4nbxpy
Black-box optimization of mixed discrete-continuous optimization problems
2017
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A popular choice is the efficient global optimization (EGO) algorithm, which is based on the prominent Kriging metamodel and the expected improvement (EI) search criterion. ...
The optimization of expensive to evaluate black-box functions is often performed with the help of model-based sequential strategies. ...
based on FANOVA. ...
doi:10.17877/de290r-17800
fatcat:klff45nnovcdvdpvil7ogkcnde
Variance-based global sensitivity analysis of numerical models using R
[article]
2022
arXiv
pre-print
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This report investigates different aspects of the variance-based global sensitivity analysis in the context of complex black-box computer codes. ...
Sensitivity analysis plays an important role in the development of computer models/simulators through identifying the contribution of each (uncertain) input factor to the model output variability. ...
Factor screening allows us to eliminate insignificant factors, especially when the model is data-driven and the number of inputs exceeds the number of model evaluations (Song et al., 2016) . ...
arXiv:2206.11348v1
fatcat:jdefhk67mvhutpjw442ul6t6xa
Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks
2021
Frontiers in Plant Science
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Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). ...
Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. ...
based on ECs, and the covariance matrix between GxE interactivity of environments obtained by AMMI decomposition. ...
doi:10.3389/fpls.2021.699589
pmid:34880880
pmcid:PMC8647909
fatcat:pivtlltmj5ajtfqtg2s5yh62la
New methods for the sensitivity analysis of black-box functions with an application to sheet metal forming
2015
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Several variance-based estimation methods are suggested. Their properties are analyzed theoretically as well as on simulations. ...
Finally, all three methods are successfully applied in the sensitivity analysis of sheet metal forming models. ...
A new Kriging model on the same data but without X 2 and X 7 is set up as updated metamodel. ...
doi:10.17877/de290r-7461
fatcat:ctxwe3hsd5bibjlraawe33pwty