A Bayesian Scheme for Reconstructing Obstacles in Acoustic Waveguides
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Article number | 53 |
Journal / Publication | Journal of Scientific Computing |
Volume | 97 |
Issue number | 3 |
Online published | 20 Oct 2023 |
Publication status | Published - Dec 2023 |
Link(s)
Abstract
In this paper, we investigate inverse obstacle scattering problems in acoustic waveguides with low-frequency data. A Bayesian inference scheme, combining a multi-fidelity strategy and surrogate model with guided modes and a deep neural network is proposed to reconstruct the shapes of unknown scattering objects. First, the inverse problem is reformulated as a statistical inference problem using Bayes’ formula, which provides statistical characteristics of the posterior distribution and quantification of the uncertainties. The well-posedness of the posterior distribution is proved by using the ƒ-divergence. Subsequently, a Markov Chain Monte Carlo algorithm is used to explore the posterior density. We propose a new multi-fidelity surrogate model to accelerate the sampling procedure while maintaining high accuracy. Our numerical simulations demonstrate that this method not only produces high-quality reconstructions but also substantially reduces computational costs. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Research Area(s)
- Acoustic waveguide, Bayesian inference, ƒ-Divergence, Machine learning
Citation Format(s)
A Bayesian Scheme for Reconstructing Obstacles in Acoustic Waveguides. / Gao, Yu; Liu, Hongyu; Wang, Xianchao et al.
In: Journal of Scientific Computing, Vol. 97, No. 3, 53, 12.2023.
In: Journal of Scientific Computing, Vol. 97, No. 3, 53, 12.2023.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review