Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

1 code implementation • 15 Mar 2024 • S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang

Existing work in scientific machine learning (SciML) has shown that data-driven learning of solution operators can provide a fast approximate alternative to classical numerical partial differential equation (PDE) solvers.

Uncertainty Quantification