For the elliptic data assimilation problem, Gaussian priors defined directly on finite element spaces give discrete posterior means that contract to the ground truth at the optimal continuous-level rates when the mesh size and the number of samples are coupled as h ~ N^{-1/(2α+d)}.
Solving the unique continuation problem for Schr\"odinger equations with low regularity solutions using a stabilized finite element method
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abstract
In this paper, we consider the unique continuation problem for the Schr\"odinger equations. We prove a H\"older type conditional stability estimate and build up a parameterized stabilized finite element scheme adaptive to the \textit{a priori} knowledge of the solution, achieving error estimates in interior domains with convergence up to continuous stability. The approximability of the scheme to solutions with only $H^1$-regularity is studied and the convergence rate for solutions with regularity higher than $H^1$ is also shown. Comparisons in terms of different parameterization for different regularities will be illustrated with respect to the convergence and condition numbers of the linear systems. Finally, numerical experiments will be given to illustrate the theory.
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Posterior contraction rates of computational methods for Bayesian data assimilation
For the elliptic data assimilation problem, Gaussian priors defined directly on finite element spaces give discrete posterior means that contract to the ground truth at the optimal continuous-level rates when the mesh size and the number of samples are coupled as h ~ N^{-1/(2α+d)}.