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Found 5 papers, 5 papers with code
DeepFlow: History Matching in the Space of Deep Generative Models
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.
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
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.
Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models
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.
Stochastic reconstruction of an oolitic limestone by generative adversarial networks
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.
Reconstruction of three-dimensional porous media using generative adversarial neural networks
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.
