Search Results for author:
Found 5 papers, 2 papers with code
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural network.
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Specifically, $L_0$+SVGD demonstrates superior resilience to noise, the ability to perform well in extrapolated regions, and a faster convergence rate to an optimal solution.
A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids.
A Bayesian Multiscale Deep Learning Framework for Flows in Random Media
In addition, it is challenging to develop accurate surrogate and uncertainty quantification models for high-dimensional problems governed by stochastic multiscale PDEs using limited training data.
Solving inverse problems using conditional invertible neural networks
In this work, we construct a two- and three-dimensional inverse surrogate models consisting of an invertible and a conditional neural network trained in an end-to-end fashion with limited training data.
