Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers

no code implementations • 23 Jul 2022 • Björn Lütjens, Catherine H. Crawford, Campbell D Watson, Christopher Hill, Dava Newman

Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution.

Operator learning Uncertainty Quantification

PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling

no code implementations • 5 May 2021 • Björn Lütjens, Catherine H. Crawford, Mark Veillette, Dava Newman

Climate models project an uncertainty range of possible warming scenarios from 1. 5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble.

Management