no code implementations • 26 Jan 2023 • Qizhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise.
no code implementations • 19 Apr 2022 • Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis
We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.
no code implementations • 10 Dec 2019 • Eric C. Cyr, Mamikon A. Gulian, Ravi G. Patel, Mauro Perego, Nathaniel A. Trask
Motivated by the gap between theoretical optimal approximation rates of deep neural networks (DNNs) and the accuracy realized in practice, we seek to improve the training of DNNs.