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Found 7 papers, 0 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.
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.
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses.
Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design
Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule).
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning
Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions.
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these models while avoiding the overwhelming computational demands associated with running a statistically significant sample of simulations.
Machine learning for graph-based representations of three-dimensional discrete fracture networks
Restricting the flowing fracture network to this backbone provides a significant reduction in the network's effective size.
