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Found 6 papers, 1 papers with code
Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration
We found that the dissolution reaction rate constant of quartz and the distance to the flowing backbone in the fracture network are the two most important features that control the amount of quartz left in the system.
Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization
Recreating complex, high-dimensional global fields from limited data points is a grand challenge across various scientific and industrial domains.
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations.
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.
Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media
The permeability of complex porous materials can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive.
Modeling nanoconfinement effects using active learning
At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.
