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Found 7 papers, 0 papers with code
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy
For black-box simulations, non-intrusive PCE allows the construction of these surrogates using a set of simulation response evaluations.
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations
In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference.
Modular machine learning-based elastoplasticity: generalization in the context of limited data
The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics.
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.
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference
In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities.
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.
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.
