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Aykroyd1.pdf

aykroyd1.pdf
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🔗 Source: eprints.whiterose.ac.uk
📊 Size: 735 KB
👤 Author: Aykroyd, RG, Lesnic, DL and Karageorghis, A
⬇️ Downloads: 96

Summary

A fully Bayesian approach is used to estimate the shape of objects from tomography data using the method of fundamental solutions (MFS) forward solutions. The object is a rigid inclusion within a homogeneous background medium, and the data consists of potential and current flux measurements made on the boundary of the region. A geometric parametrization is used to describe the object, and a Bayesian modelling approach combines data likelihood and prior information. Posterior estimation is based on a Markov chain Monte Carlo algorithm, providing measures of uncertainty and point estimates. The MFS is used to solve the forward problem, and numerical results demonstrate that accurate results are possible with appropriate choices of prior distributions. The approach allows for an intuitive model description and directly interpretable output, and can be easily applied to other inverse problems.

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Description

A fully Bayesian approach is used to estimate the shape of objects from tomography data using the method of fundamental solutions (MFS) forward solutions.

Technical Information

  • File Format: PDF
  • File Size: 735 KB
  • Pages: 22
  • Language: EN
  • Author: Aykroyd, RG, Lesnic, DL and Karageorghis, A
  • Total Downloads: 96
  • Last Updated: 3 weeks ago

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