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Found 8 papers, 1 papers with code
Efficient machine-learning surrogates for large-scale geological carbon and energy storage
Geological carbon and energy storage are pivotal for achieving net-zero carbon emissions and addressing climate change.
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer
Data-driven modeling can suffer from a constant demand for data, leading to reduced accuracy and impractical for engineering applications due to the high cost and scarcity of information.
Subsurface Characterization using Ensemble-based Approaches with Deep Generative Models
WGAN-GP is trained to generate high-dimensional K fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements.
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
Through a series of benchmark problems of natural convection in porous media, BT-AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds.
Machine Learning in Heterogeneous Porous Materials
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.
Applications of physics-informed scientific machine learning in subsurface science: A survey
Geosystems are geological formations altered by humans activities such as fossil energy exploration, waste disposal, geologic carbon sequestration, and renewable energy generation.
Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Networks
The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process.
Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks
Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation.
