Efficient posterior estimation for stochastic SHM using transport maps Article Swipe
Jan Grashorn
,
Matteo Broggi
,
Ludovic Chamoin
,
Michael Beer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1201/9781003323020-82
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1201/9781003323020-82
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.
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- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1201/9781003323020-82
- https://api.taylorfrancis.com/content/chapters/oa-edit/download?identifierName=doi&identifierValue=10.1201/9781003323020-82&type=chapterpdf
- OA Status
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- References
- 11
- Related Works
- 10
- OpenAlex ID
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- DOI
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https://doi.org/10.1201/9781003323020-82Digital Object Identifier
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Efficient posterior estimation for stochastic SHM using transport mapsWork title
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book-chapterOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-28Full publication date if available
- Authors
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Jan Grashorn, Matteo Broggi, Ludovic Chamoin, Michael BeerList of authors in order
- Landing page
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https://doi.org/10.1201/9781003323020-82Publisher landing page
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https://api.taylorfrancis.com/content/chapters/oa-edit/download?identifierName=doi&identifierValue=10.1201/9781003323020-82&type=chapterpdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://api.taylorfrancis.com/content/chapters/oa-edit/download?identifierName=doi&identifierValue=10.1201/9781003323020-82&type=chapterpdfDirect OA link when available
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Estimation, Computer science, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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