Paired Autoencoders for Likelihood-free Estimation in Inverse Problems
This work addresses a specific computational challenge in solving inverse problems for domains like geophysics and imaging, offering an incremental improvement over existing likelihood-free methods.
The authors tackled the computational bottleneck of estimating data misfit in nonlinear inverse problems by proposing a paired autoencoder framework as a likelihood-free estimator, demonstrating its viability in full waveform inversion and inverse electromagnetic imaging with improved solution quality.
We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to solve in practice and require minimizing a combination of a data-fit term and a regularization term. The main computational bottleneck of typical algorithms is the direct estimation of the data misfit. Therefore, likelihood-free approaches have become appealing alternatives. Nonetheless, difficulties in generalization and limitations in accuracy have hindered their broader utility and applicability. In this work, we use a paired autoencoder framework as a likelihood-free estimator for inverse problems. We show that the use of such an architecture allows us to construct a solution efficiently and to overcome some known open problems when using likelihood-free estimators. In particular, our framework can assess the quality of the solution and improve on it if needed. We demonstrate the viability of our approach using examples from full waveform inversion and inverse electromagnetic imaging.