Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 |
| Herausgeber/-innen | Fabio Biondini, Dan M. Frangopol |
| Seiten | 678-685 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 - Milan, Italien Dauer: 2 Juli 2023 → 6 Juli 2023 |
Publikationsreihe
| Name | Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 |
|---|
Abstract
Accurate parameter estimation is a challenging task that demands realistic models and algorithms to obtain the parameter’s probability distributions. The Bayesian theorem in conjunction with sampling methods proved to be invaluable here since it allows for the formulation of the problem in a probabilistic framework. This opens up the possibilities of using prior information and knowledge about parameter distributions as well as the natural incorporation of aleatory and epistemic uncertainties. Traditionally, Markov Chain Monte Carlo (MCMC) methods are used to approximate the posterior distribution of samples given some data. However, these methods usually need a large amount of samples and therefore a large amount of model evaluations. Recent advances in transport theory and its application in the context of Bayesian model updating (BMU) make it possible to approximate the posterior distribution analytically and hence eliminate the need for sampling methods. This paves the way for the usage in real-time applications and for fast parameter estimation. We investigate here the application of transport maps to a simple analytical model as well as a structural dynamics model. The performance is compared to an MCMC approach to assess the accuracy and efficiency of transport maps. A discussion about requirements for the implementation of transport maps as well as details on the implementation are also given.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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- BibTex
- RIS
Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023. Hrsg. / Fabio Biondini; Dan M. Frangopol. 2023. S. 678-685 (Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Efficient posterior estimation for stochastic SHM using transport maps
AU - Grashorn, J.
AU - Broggi, M.
AU - Chamoin, L.
AU - Beer, M.
N1 - Funding Information: Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for the GRK2657 (grant reference number 433082294) is greatly appreciated.
PY - 2023
Y1 - 2023
N2 - Accurate parameter estimation is a challenging task that demands realistic models and algorithms to obtain the parameter’s probability distributions. The Bayesian theorem in conjunction with sampling methods proved to be invaluable here since it allows for the formulation of the problem in a probabilistic framework. This opens up the possibilities of using prior information and knowledge about parameter distributions as well as the natural incorporation of aleatory and epistemic uncertainties. Traditionally, Markov Chain Monte Carlo (MCMC) methods are used to approximate the posterior distribution of samples given some data. However, these methods usually need a large amount of samples and therefore a large amount of model evaluations. Recent advances in transport theory and its application in the context of Bayesian model updating (BMU) make it possible to approximate the posterior distribution analytically and hence eliminate the need for sampling methods. This paves the way for the usage in real-time applications and for fast parameter estimation. We investigate here the application of transport maps to a simple analytical model as well as a structural dynamics model. The performance is compared to an MCMC approach to assess the accuracy and efficiency of transport maps. A discussion about requirements for the implementation of transport maps as well as details on the implementation are also given.
AB - Accurate parameter estimation is a challenging task that demands realistic models and algorithms to obtain the parameter’s probability distributions. The Bayesian theorem in conjunction with sampling methods proved to be invaluable here since it allows for the formulation of the problem in a probabilistic framework. This opens up the possibilities of using prior information and knowledge about parameter distributions as well as the natural incorporation of aleatory and epistemic uncertainties. Traditionally, Markov Chain Monte Carlo (MCMC) methods are used to approximate the posterior distribution of samples given some data. However, these methods usually need a large amount of samples and therefore a large amount of model evaluations. Recent advances in transport theory and its application in the context of Bayesian model updating (BMU) make it possible to approximate the posterior distribution analytically and hence eliminate the need for sampling methods. This paves the way for the usage in real-time applications and for fast parameter estimation. We investigate here the application of transport maps to a simple analytical model as well as a structural dynamics model. The performance is compared to an MCMC approach to assess the accuracy and efficiency of transport maps. A discussion about requirements for the implementation of transport maps as well as details on the implementation are also given.
UR - http://www.scopus.com/inward/record.url?scp=85186700442&partnerID=8YFLogxK
U2 - 10.1201/9781003323020-82
DO - 10.1201/9781003323020-82
M3 - Conference contribution
AN - SCOPUS:85186700442
SN - 9781003323020
T3 - Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
SP - 678
EP - 685
BT - Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
A2 - Biondini, Fabio
A2 - Frangopol, Dan M.
T2 - 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
Y2 - 2 July 2023 through 6 July 2023
ER -