A Bayesian Multiscale Deep Learning Framework for Flows in Random Media

1 code implementation • 8 Mar 2021 • Govinda Anantha Padmanabha, Nicholas Zabaras

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.

Uncertainty Quantification

Solving inverse problems using conditional invertible neural networks

1 code implementation • 31 Jul 2020 • Govinda Anantha Padmanabha, Nicholas Zabaras

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.