Learning to regularize with a variational autoencoder for hydrologic inverse analysis
D. O'Malley, J.K. Golden, V.V. Vesselinov
arXiv2021DOI arXiv:1906.02401v1
Summary
Inverse problems often involve matching observational data using a physical model that takes a large number of parameters as input. These problems tend to be under-constrained and require regularization to impose additional structure on the solution in parameter space. A central difficulty in regularization is turning a complex conceptual model of this additional structure into a functional mathematical form to be used in the inverse analysis.