Solving inverse problems using conditional invertible neural networks

Datasets for the two-and three-dimensional problem in the paper: Solving inverse problems using conditional invertible neural networks.

In this work, we construct two- and three-dimensional inverse surrogate models consisting of an invertible and a conditional neural network trained in an end-to-end fashion with limited training data. The invertible network is developed using a flow-based generative model. The developed inverse surrogate model is then applied for an inversion task of a multiphase flow problem where given the pressure and saturation observations the aim is to recover a high-dimensional non-Gaussian log-permeability field where the two facies consist of heterogeneous log-permeability and varying length-scales.