19 citations · 26 across the 19 of their papers we have counts for
6 papers · 1 filter
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Cosmin Safta, Reese E. Jones, Ravi G. Patel +4
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differ…
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
Govinda Anantha Padmanabha, Cosmin Safta, Nikolaos Bouklas +1
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 n…
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta +2
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagu…
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
Jeremiah Hauth, Cosmin Safta, Xun Huan +2
The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at mode…
Deep learning and multi-level featurization of graph representations of microstructural data
Reese Jones, Cosmin Safta, Ari Frankel
Many material response functions depend strongly on microstructure, such as inhomogeneities in phase or orientation. Homogenization presents the task of predicting the mean respons…
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Laura Swiler, Mamikon Gulian, Ari Frankel +2
Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources. As part of a broader effort in scientific machine learning, many recen…