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Found 12 papers, 4 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.
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.
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.
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.
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator of partial differential equations (PDEs).
Boolean Hierarchical Tucker Networks on Quantum Annealers
Quantum annealing is an emerging technology with the potential to solve some of the computational challenges that remain unresolved as we approach an era beyond Moore's Law.
Quantum Physics
Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture
Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive.
Reverse Annealing for Nonnegative/Binary Matrix Factorization
It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms.
Learning to regularize with a variational autoencoder for hydrologic inverse analysis
A central difficulty in regularization is turning a complex conceptual model of this additional structure into a functional mathematical form to be used in the inverse analysis.
Nonnegative/binary matrix factorization with a D-Wave quantum annealer
Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method.
