Search Results for author: Ryan Lagerquist

Found 2 papers, 0 papers with code

Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?

no code implementations21 Mar 2022 Ryan Lagerquist, Imme Ebert-Uphoff

We provide a general guide to using SELFs, including technical challenges and the final Python code, as well as demonstrating their use for the convection problem.

CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

no code implementations17 Jun 2021 Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn, Yoonjin Lee, Katherine Haynes, Jason Stock, Christina Kumler, Jebb Q. Stewart

Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification.

Cannot find the paper you are looking for? You can Submit a new open access paper.