Search Results for author:
Found 8 papers, 5 papers with code
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications
These methods are consistently applied to fault detection case studies where Deep Ensembles use independently trained models to provide fault probabilities, Concrete Dropout represents an extension to the popular Dropout technique to approximate Bayesian neural networks, and finally, we apply SWAG, a recent method that is based on the Bayesian inference equivalence of mini-batch Stochastic Gradient Descent.
Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices.
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.
Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks
Traditional physics-based approaches to infer sub-surface properties such as full-waveform inversion or reflectivity inversion are time-consuming and computationally expensive.
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.
