Sampling in Bayesian Inversion Accelerated by Surrogate Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F25%3A00642868" target="_blank" >RIV/68145535:_____/25:00642868 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-87213-6_44" target="_blank" >https://doi.org/10.1007/978-3-031-87213-6_44</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-87213-6_44" target="_blank" >10.1007/978-3-031-87213-6_44</a>

Result language
angličtina
Original language name
Sampling in Bayesian Inversion Accelerated by Surrogate Models
Original language description
This chapter explores the acceleration of Bayesian inversion for geotechnical problems using surrogate models, with a focus on neural networks. The study introduces a sampling framework based on the delayed-acceptance Metropolis-Hastings (DAMH) algorithm, which is enhanced with surrogate models to significantly improve efficiency. The framework is applied to a real-world geotechnical inverse problem derived from a tunnel sealing experiment, demonstrating its practical relevance. The chapter compares various surrogate models, including neural networks, radial basis functions, and polynomial chaos approximation, highlighting the superior performance of neural network-based approaches. The results show that refining surrogate models during the sampling process can greatly reduce the number of rejected samples, leading to increased sampling efficiency. The posterior distribution is analyzed and visualized, providing insights into the model's performance and the quality of the surrogate models. The conclusions emphasize the promise of neural network surrogate models for accelerating Bayesian inversion in geotechnical engineering, while noting the importance of careful network configuration and learning method settings.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Inverse Problems: Modelling and Simulation : Extended Abstracts of the IPMS Conference 2024
ISBN
978-3-031-87212-9
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
365-371
Publisher name
Springer
Place of publication
Cham
Event location
Paradise Bay Resort Hotel, Malta
Event date
May 26, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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