Increases in wildfire activity and the resulting impacts have prompted t...
We present a deep learning-based computational algorithm for inversion o...
The solution of probabilistic inverse problems for which the correspondi...
These notes were compiled as lecture notes for a course developed and ta...
Operator networks have emerged as promising deep learning tools for
appr...
In this work, we train conditional Wasserstein generative adversarial
ne...
Inverse problems are notoriously difficult to solve because they can hav...
In this paper, we present an efficient numerical algorithm for solving t...
A numerical method using discontinuous polynomial approximations is
form...
We establish a notion of random entropy solution for degenerate fraction...
We present a novel active learning algorithm, termed as iterative surrog...
Deep neural networks and the ENO procedure are both efficient frameworks...
Neural networks are increasingly used in complex (data-driven) simulatio...
Many large scale problems in computational fluid dynamics such as uncert...