Sequential optimization and uncertainty propagation method for efficient optimization-based model calibration
Abstract The goal of model calibration is to improve the predictive capability of a computational model by estimating the unknown input variables of the model. Optimization-based model calibration (OBMC) is a probabilistic way to estimate the unknown input variables through the use of optimization t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Guesuk [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Optimization-based model calibration (OBMC) Optimization under uncertainty (OUU) |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Structural and multidisciplinary optimization - Springer Berlin Heidelberg, 2000, 60(2019), 4 vom: 10. Aug., Seite 1355-1372 |
|---|---|
| Übergeordnetes Werk: |
volume:60 ; year:2019 ; number:4 ; day:10 ; month:08 ; pages:1355-1372 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00158-019-02351-2 |
|---|
| Katalog-ID: |
OLC2051790469 |
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| 520 | |a Abstract The goal of model calibration is to improve the predictive capability of a computational model by estimating the unknown input variables of the model. Optimization-based model calibration (OBMC) is a probabilistic way to estimate the unknown input variables through the use of optimization techniques. Performing optimization in a probabilistic sense requires a high computational cost to obtain statistics about the outputs at every iteration of the optimization. To improve optimization efficiency, this paper proposes a sequential optimization-based model calibration approach that makes use of first an efficient, and then a highly accurate probabilistic assessment method, in sequence. At the earlier stage of the sequential optimizations, approximate integration methods are used to accelerate the probabilistic assessment process. As a calibration metric, the moment matching metric is devised to use the obtained statistics of the outputs. When the optimization reaches near-convergence, a more accurate method, such as a sampling method with an accurate surrogate model, is substituted for the probabilistic assessment. Thus, this paper provides an efficient and accurate procedure for optimization-based model calibration. Two engineering applications, model calibration of a shallow strip footing model and an automotive steering wheel-column model, are presented to demonstrate the effectiveness of the proposed method. | ||
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Sequential optimization and uncertainty propagation method for efficient optimization-based model calibration |
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Sequential optimization and uncertainty propagation method for efficient optimization-based model calibration |
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sequential optimization and uncertainty propagation method for efficient optimization-based model calibration |
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Sequential optimization and uncertainty propagation method for efficient optimization-based model calibration |
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Abstract The goal of model calibration is to improve the predictive capability of a computational model by estimating the unknown input variables of the model. Optimization-based model calibration (OBMC) is a probabilistic way to estimate the unknown input variables through the use of optimization techniques. Performing optimization in a probabilistic sense requires a high computational cost to obtain statistics about the outputs at every iteration of the optimization. To improve optimization efficiency, this paper proposes a sequential optimization-based model calibration approach that makes use of first an efficient, and then a highly accurate probabilistic assessment method, in sequence. At the earlier stage of the sequential optimizations, approximate integration methods are used to accelerate the probabilistic assessment process. As a calibration metric, the moment matching metric is devised to use the obtained statistics of the outputs. When the optimization reaches near-convergence, a more accurate method, such as a sampling method with an accurate surrogate model, is substituted for the probabilistic assessment. Thus, this paper provides an efficient and accurate procedure for optimization-based model calibration. Two engineering applications, model calibration of a shallow strip footing model and an automotive steering wheel-column model, are presented to demonstrate the effectiveness of the proposed method. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Abstract The goal of model calibration is to improve the predictive capability of a computational model by estimating the unknown input variables of the model. Optimization-based model calibration (OBMC) is a probabilistic way to estimate the unknown input variables through the use of optimization techniques. Performing optimization in a probabilistic sense requires a high computational cost to obtain statistics about the outputs at every iteration of the optimization. To improve optimization efficiency, this paper proposes a sequential optimization-based model calibration approach that makes use of first an efficient, and then a highly accurate probabilistic assessment method, in sequence. At the earlier stage of the sequential optimizations, approximate integration methods are used to accelerate the probabilistic assessment process. As a calibration metric, the moment matching metric is devised to use the obtained statistics of the outputs. When the optimization reaches near-convergence, a more accurate method, such as a sampling method with an accurate surrogate model, is substituted for the probabilistic assessment. Thus, this paper provides an efficient and accurate procedure for optimization-based model calibration. Two engineering applications, model calibration of a shallow strip footing model and an automotive steering wheel-column model, are presented to demonstrate the effectiveness of the proposed method. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Abstract The goal of model calibration is to improve the predictive capability of a computational model by estimating the unknown input variables of the model. Optimization-based model calibration (OBMC) is a probabilistic way to estimate the unknown input variables through the use of optimization techniques. Performing optimization in a probabilistic sense requires a high computational cost to obtain statistics about the outputs at every iteration of the optimization. To improve optimization efficiency, this paper proposes a sequential optimization-based model calibration approach that makes use of first an efficient, and then a highly accurate probabilistic assessment method, in sequence. At the earlier stage of the sequential optimizations, approximate integration methods are used to accelerate the probabilistic assessment process. As a calibration metric, the moment matching metric is devised to use the obtained statistics of the outputs. When the optimization reaches near-convergence, a more accurate method, such as a sampling method with an accurate surrogate model, is substituted for the probabilistic assessment. Thus, this paper provides an efficient and accurate procedure for optimization-based model calibration. Two engineering applications, model calibration of a shallow strip footing model and an automotive steering wheel-column model, are presented to demonstrate the effectiveness of the proposed method. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Sequential optimization and uncertainty propagation method for efficient optimization-based model calibration |
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