Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems

In this paper, we consider a Bayesian method for nonlinear elastic inverse problems. As a working model, we are interested in the inverse problem of restoring elastic properties from measured tissue displacement. In order to reduce the computational cost, we will use the following multi-fidelity model approach. First, we construct a surrogate low-fidelity DNNs-based model in the prior distribution, then use a certain number of simulations of high fidelity model associated with an adaptive strategy online to update the low-fidelity model locally. Numerical examples show that the proposed method can solve nonlinear elastic inverse problems efficiently and accurately.

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Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems

Cited by 2 publications

References 50 publications

Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography

Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography

Adaptive neural network surrogate model for solving the nonlinear elastic inverse problem via Bayesian inference

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