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Found 14 papers, 8 papers with code
Generating Infinite-Size Textures using GANs with Patch-by-Patch Paradigm
Existing texture synthesis techniques rely on generating large-scale textures using a single forward pass to the generative model; this approach limits the scalability and flexibility of the images produced.
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control
Active flow control (AFC) involves manipulating fluid flow over time to achieve a desired performance or efficiency.
Stochastic optimal well control in subsurface reservoirs using reinforcement learning
For this problem, the system is partially observed since the data is only available at well locations.
Robust optimal well control using an adaptive multi-grid reinforcement learning framework
RL control policies are initially learned using computationally efficient low fidelity simulations using coarse grid discretization of the underlying partial differential equations (PDEs).
Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network
Probabilistic forecasts are generated using an ensemble of equiprobable model vector realizations.
Generation of non-stationary stochastic fields using Generative Adversarial Networks
In this work, we investigate the problem of using Generative Adversarial Networks (GANs) models to generate non-stationary geological channelized patterns and examine the models generalization capability at new spatial modes that were never seen in the given training set.
Generating unrepresented proportions of geological facies using Generative Adversarial Networks
In this work, we investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset.
Direct multi-modal inversion of geophysical logs using deep learning
Geosteering of wells requires fast interpretation of geophysical logs, which is a non-unique inverse problem.
Optimal Bayesian experimental design for subsurface flow problems
In other words, these techniques explore the space of possible observations and determine an experimental setup that produces maximum information about the system parameters on average.
Parametrization of stochastic inputs using generative adversarial networks with application in geology
The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations.
Exemplar-based synthesis of geology using kernel discrepancies and generative neural networks
We synthesize new realizations such that the discrepancy in the patch distribution between the realizations and the exemplar image is minimized.
Parametric generation of conditional geological realizations using generative neural networks
This inference network is a neural network trained to sample a posterior distribution derived using a Bayesian formulation of the conditioning task.
A machine learning approach for efficient uncertainty quantification using multiscale methods
Several multiscale methods account for sub-grid scale features using coarse scale basis functions.
Parametrization and generation of geological models with generative adversarial networks
One of the main challenges in the parametrization of geological models is the ability to capture complex geological structures often observed in the subsurface.
