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Found 7 papers, 1 papers with code
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial-temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant.
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.
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.
Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification
To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population.
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.
Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ.
