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Found 5 papers, 0 papers with code
Deep learning and multi-level featurization of graph representations of microstructural data
We develop a means of deep learning of hidden features on the reduced graph given the native discretization and a segmentation of the initial input field.
Mesh-based graph convolutional neural networks for modeling materials with microstructure
Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization.
Gaussian Process Regression constrained by Boundary Value Problems
The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and boundary conditions, but for which only scattered observations of the source term are available.
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources.
Tensor Basis Gaussian Process Models of Hyperelastic Materials
Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential.
