Shannon-type expected information gain can be used to evaluate the
relevance of a proposed experiment subjected to uncertainty. The
estimation of such gain, however, relies on a double-loop integration.
Moreover, its numerical integration in multi-dimensional cases, e.g.,
when using Monte Carlo sampling methods, is therefore computationally
too expensive for realistic physical models, especially for those
involving the solution of partial differential equations. In this work,
we present a new methodology, based on the Laplace approximation for the
integration of the posterior probability density function (pdf), to
accelerate the estimation of the expected information gains in the model
parameters and predictive quantities of interest. We obtain a
Recent comments