Pareto-Based Multi-output Model Type Selection

Gorissen, Dirk; Couckuyt, Ivo; Crombecq, Karel; Dhaene, Tom

doi:10.1007/978-3-642-02319-4_53

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…The main drawback of Sobol Indices is that many simulations are required. To overcome this problem the SUMO (Surrogate MOdeling) tool [22] of Ghent University is used to generate a Kriging based surrogate model. This model generates 12 (the number of variable parameters in Table 1) dimensional surfaces that define the correlation of the input variables in perspective to a damage criteria.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Sensitivity of the hygrothermal behaviour of homogeneous masonry constructions: from Sobol indices to decision trees

Calle

Bossche

2020

E3S Web Conf.

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Historic masonry constructions are difficult to mimic in hygrothermal models. Next to the usual uncertainties on the input of hygrothermal models as the outdoor/indoor climate also the properties of the wall themselves are often highly uncertain due to the natural origin of the aggregates and the various, manual production processes used through time. Therefore, this paper presents a probabilistic analysis that indicates the sensitivity of several damage criteria which are often encountered in practice such as mould growth at the interior surface, frost damage, and potential decay of wooden beam heads. The analysis is based on 1D simulations, including realistic variations on climate parameters as wall properties. With Kriging based surrogate modelling the output of the probabilistic simulations is translated into sensitivity indices, Total Sobol indices. These indices summarize the dependency of the damage criteria for each of the input parameters including multi order effects. The Total Sobol indices indicate a generally high dependency of each of the damage criteria on the rain intensity, the trend of the moisture retention/liquid conductivity curve and the absorption coefficient. Based on the probabilistic output binary flowcharts are generated to indicate for which combinations of input parameters high risks are to be expected. These binary flowcharts can be adopted by e.g. engineering firms to define whether, a more detailed assessment is required, and which input are necessary. This indicates when basic in situ assessments of the hygrothermal properties of the facade can suffice.

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Section: Sensitivity Analysismentioning

confidence: 99%

Sensitivity of the hygrothermal behaviour of homogeneous masonry constructions: from Sobol indices to decision trees

Calle

Bossche

2020

E3S Web Conf.

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“…A Pareto based approach to multi-output modeling also allows integration with the automatic surrogate model type selection algorithm described in [39]. This enables automatic selection of the best model type (Artificial Neural Network (ANN), Kriging, Support Vector Machine (SVM), ...) for each output without having to resort to multiple runs [36,37].…”

Section: Modeling Multiple Outputsmentioning

confidence: 99%

“…We use this example to briefly illustrate the automatic model type selection per output. For a more extensive example see [36].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective global surrogate modeling, dealing with the 5-percent problem

Gorissen

Couckuyt

Laermans

et al. 2009

Engineering with Computers

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When dealing with computationally expensive simulation codes or process measurement data, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization, prototyping and optimization. Typically the model parameter (=hy-perparameter) optimization problem as part of global surrogate modeling is formulated in a single objective way. Models are generated according to a single objective (accuracy). However, this requires an engineer to determine a single accuracy target and measure upfront, which is hard to do if the behavior of the response is unknown. Likewise, the different outputs of a multi-output system are typically modeled separately by independent models. Again, a multiobjective approach would benefit the domain expert by giving information about output correlation and enabling automatic model type selection for each output dynamically. With this paper the authors attempt to increase awareness of the subtleties involved and discuss a number of solutions and applications. In particular we present a multiobjective framework for automatic global surrogate model generation to help tackle both problems and that is applicable in both the static and sequential design (adaptive sampling) case.

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Pareto-Based Multi-output Model Type Selection

Cited by 2 publications

References 10 publications

Sensitivity of the hygrothermal behaviour of homogeneous masonry constructions: from Sobol indices to decision trees

Sensitivity of the hygrothermal behaviour of homogeneous masonry constructions: from Sobol indices to decision trees

Multiobjective global surrogate modeling, dealing with the 5-percent problem

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