Abstract Generally speaking, inverse problems typically consist in the reconstruction of causes for observed effects. In imaging applications the cause is usually a probe and the effect are observed data. The corresponding forward problems then consists in predicting experimental data given perfect knowledge of the probe. In some sense solving an inverse problems means “computing backwards”, which is usually more difficult then solving the forward problem.