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| Description
| - Meta-models (or surrogate models, formerly response surfaces) are getting popular in engineering designs. They are used to simulate the behaviour of structures with less computational demands than the original model (e.g. finite element models). It is still necessary to evaluate this expensive original model few times in some specified points called Design of Experiments (DoE). The first DoE is usually just space-filling to cover up the whole design space and the meta-model built with an initial DoE is therefore not very accurate everywhere. Recently, a new updating procedure of a general surrogate model has been proposed. New points are added to the meta-model to improve accuracy at important regions based on a result of multi-objective optimization. There are two criteria: first, a new support point should be in the vicinity of the limit state function. Second, to bring the maximum new information, the point should be far from other points, which is performed by appropriate distance metric. The final Pareto-front is clustered and the best points are added to DoE to train updated meta-model. In this contribution, various metamodels are reviewed for their suitability for the proposed updating procedure. Their comparison is then conducted on a simple reliability-based optimization task.
- Meta-models (or surrogate models, formerly response surfaces) are getting popular in engineering designs. They are used to simulate the behaviour of structures with less computational demands than the original model (e.g. finite element models). It is still necessary to evaluate this expensive original model few times in some specified points called Design of Experiments (DoE). The first DoE is usually just space-filling to cover up the whole design space and the meta-model built with an initial DoE is therefore not very accurate everywhere. Recently, a new updating procedure of a general surrogate model has been proposed. New points are added to the meta-model to improve accuracy at important regions based on a result of multi-objective optimization. There are two criteria: first, a new support point should be in the vicinity of the limit state function. Second, to bring the maximum new information, the point should be far from other points, which is performed by appropriate distance metric. The final Pareto-front is clustered and the best points are added to DoE to train updated meta-model. In this contribution, various metamodels are reviewed for their suitability for the proposed updating procedure. Their comparison is then conducted on a simple reliability-based optimization task. (en)
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| Title
| - Comparison of Adaptively Updated Surrogate Models for Reliability Analysis
- Comparison of Adaptively Updated Surrogate Models for Reliability Analysis (en)
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| skos:prefLabel
| - Comparison of Adaptively Updated Surrogate Models for Reliability Analysis
- Comparison of Adaptively Updated Surrogate Models for Reliability Analysis (en)
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| skos:notation
| - RIV/68407700:21110/14:00219435!RIV15-MSM-21110___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21110/14:00219435
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Surrogate models; Adaptive update; Reliability Assessment; Design of Experiments (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Lepš, Matěj
- Pospíšilová, Adéla
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| http://localhost/t...ganizacniJednotka
| |
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