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Probabilistic Modeling of Heteroscedastic Laboratory Experiments Using Gaussian Process Regression
This paper proposes an extension to Gaussian process regression (GPR) for data sets composed of only a few replicated specimens and displaying a heteroscedastic behavior. Because there are several factors that are out of the control of experimenters, it is often impossible to reproduce identical specimens for a same experiment. Moreover, observations from laboratory experiments typically display a heteroscedastic interspecimen variability. Because experiments and specimen manufacturing are expensive, it is uncommon to have more than three specimens to build a model for the observed responses. The method proposed in this paper uses GPR to predict each tested specimen using a shared prior structure and models the global heteroscedastic behavior by combining observations using conjugate prior distributions. An application of the method to high-performance fiber-reinforced concrete experiments highlights fiber addition benefits for reducing water permeability caused by macrocracks.
Probabilistic Modeling of Heteroscedastic Laboratory Experiments Using Gaussian Process Regression
This paper proposes an extension to Gaussian process regression (GPR) for data sets composed of only a few replicated specimens and displaying a heteroscedastic behavior. Because there are several factors that are out of the control of experimenters, it is often impossible to reproduce identical specimens for a same experiment. Moreover, observations from laboratory experiments typically display a heteroscedastic interspecimen variability. Because experiments and specimen manufacturing are expensive, it is uncommon to have more than three specimens to build a model for the observed responses. The method proposed in this paper uses GPR to predict each tested specimen using a shared prior structure and models the global heteroscedastic behavior by combining observations using conjugate prior distributions. An application of the method to high-performance fiber-reinforced concrete experiments highlights fiber addition benefits for reducing water permeability caused by macrocracks.
Probabilistic Modeling of Heteroscedastic Laboratory Experiments Using Gaussian Process Regression
Tabor, Lucie (author) / Goulet, James-A. (author) / Charron, Jean-Philippe (author) / Desmettre, Clelia (author)
2018-04-11
Article (Journal)
Electronic Resource
Unknown
| Elsevier | 2024
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