Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification

Understand

We propose a novel deep learning based surrogate model for solving high-dimensional uncertainty quantification and uncertainty propagation problems.

• The proposed deep learning architecture is developed by integrating the well-known U-net architecture with the Gaussian Gated Linear Network (GGLN) and referred to as the Gated Linear Network induced U-net or GLU-net.
• The proposed GLU-net treats the uncertainty propagation problem as an image to image regression and hence, is extremely data efficient.
• Additionally, it also provides estimates of the predictive uncertainty.