Search Results for author:
Found 6 papers, 1 papers with code
Bayesian tomography using polynomial chaos expansion and deep generative networks
By sampling a low-dimensional prior probability distribution defined in the low-dimensional latent space of such a model, it becomes possible to efficiently sample the physical domain at the price of a generator that is typically highly non-linear.
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors
Furthermore, in that context, covariance matrices can become too large to be stored.
Fast ABC with joint generative modelling and subset simulation
We propose a novel approach for solving inverse-problems with high-dimensional inputs and an expensive forward mapping.
Gradient-based deterministic inversion of geophysical data with Generative Adversarial Networks: is it feasible?
Global probabilistic inversion within the latent space learned by Generative Adversarial Networks (GAN) has been recently demonstrated (Laloy et al., 2018).
Geophysics
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media.
Training-image based geostatistical inversion using a spatial generative adversarial neural network
After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds.
