Title: Bernstein - von Mises theorems for statistical inverse problems I: Schrödinger equation,
Abstract: The inverse problem of determining the unknown potential $f>0$ in the partial
differential equation $$\frac{\Delta}{2} u - fu =0 \text{ on } \mathcal O
~~\text{s.t. } u = g \text { on } \partial \mathcal O,$$ where $\mathcal O$ is
a bounded $C^\infty$-domain in $\mathbb R^d$ and $g>0$ is a given function
prescribing boundary values, is considered. The data consist of the solution
$u$ corrupted by additive Gaussian noise. A nonparametric Bayesian prior for
the function $f$ is devised and a Bernstein - von Mises theorem is proved which
entails that the posterior distribution given the observations is approximated
in a suitable function space by an infinite-dimensional Gaussian measure that
has a `minimal' covariance structure in an information-theoretic sense. As a
consequence the posterior distribution performs valid and optimal frequentist
statistical inference on $f$ in the small noise limit.