1 code implementation • 14 May 2019 • Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Our contribution shows that for a synthetic test case, we are able to obtain solutions to the inverse problem by optimising in the latent variable space of a deep generative model, given a set of transient observations of a non-linear forward problem.
1 code implementation • 10 Jun 2018 • Lukas Mosser, Olivier Dubrule, Martin J. Blunt
We show that approximate MALA sampling allows efficient Bayesian inversion of model parameters obtained from a prior represented by a deep generative model, obtaining a diverse set of realizations that reflect the observed seismic response.
1 code implementation • 15 Feb 2018 • Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Based on the previous work of Yeh et al. (2016), we use a content loss to constrain to the conditioning data and a perceptual loss obtained from the evaluation of the GAN discriminator network.
1 code implementation • 7 Dec 2017 • Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification.
2 code implementations • 11 Apr 2017 • Lukas Mosser, Olivier Dubrule, Martin J. Blunt
We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets.