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Multilevel Markov Chain Monte Carlo for Bayesian inverse problem for Navier-Stokes equation

2022
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Inverse Problems and Imaging
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<p style='text-indent:20px;'>Bayesian inverse problems for inferring the unknown forcing and initial condition of Navier-Stokes equation play important roles in many practical areas. The computation cost of sampling the posterior probability measure can be exceedingly high. We develop the Finite Element Multilevel Markov Chain Monte Carlo (FE-MLMCMC) sampling method for approximating expectation with respect to the posterior probability measure of quantities of interest for a model problem of

doi:10.3934/ipi.2022033
fatcat:rphavpzwg5gzbg63gw4v4chsp4