no code implementations • 15 Jan 2024 • Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis
Multiscale problems are challenging for neural network-based discretizations of differential equations, such as physics-informed neural networks (PINNs).
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 • 17 Jun 2021 • Amanda A. Howard, Alexandre M. Tartakovsky
Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently, however, there is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance.
no code implementations • 26 Aug 2020 • Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.