Quantifying Spatial Variability of Information in Inverse Problems to Improve Reconstruction Accuracy

The information density, based on the variance of reconstructed parameters, can be used to quantify the spatial variability of information available for accurately recovering parameters in inverse problems. This information density can guide practical algorithms for solving inverse problems, such as choosing the discretization mesh.