389 citations · 732 across the 6 of their papers we have counts for
6 papers
Speeding up reactive transport simulations in cement systems by surrogate geochemical modeling: deep neural networks and k-nearest neighbors
Eric Laloy, Diederik Jacques
We accelerate reactive transport (RT) simulation by replacing the geochemical solver in the RT code by a surrogate model or emulator, considering either a trained deep neural netwo…
Approaching geoscientific inverse problems with vector-to-image domain transfer networks
Eric Laloy, Niklas Linde, Diederik Jacques
We present vec2pix, a deep neural network designed to predict categorical or continuous 2D subsurface property fields from one-dimensional measurement data (e.g., time series), the…
Gradient-based deterministic inversion of geophysical data with Generative Adversarial Networks: is it feasible?
Eric Laloy, Niklas Linde, Cyprien Ruffino +3
Global probabilistic inversion within the latent space learned by a Generative Adversarial Network (GAN) has been recently demonstrated. Compared to inversion on the original model…
Emulation of CPU-demanding reactive transport models: comparison of Gaussian processes, polynomial chaos expansion and deep neural networks
Eric Laloy, Diederik Jacques
This paper presents a detailed comparison between 3 methods for emulating CPU-intensive reactive transport models (RTMs): Gaussian processes (GPs), polynomial chaos expansion (PCE)…
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Eric Laloy, Romain Hérault, John Lee +2
Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational a…
Training-image based geostatistical inversion using a spatial generative adversarial neural network
Eric Laloy, Romain Hérault, Diederik Jacques +1
Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and ev…