Search Results for author:
Found 4 papers, 2 papers with code
Knowledge-Based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows
Physics-informed neural networks (PINNs) have gained significant prominence as a powerful tool in the field of scientific computing and simulations.
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges
Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities.
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.
