Using Machine Learning for Model Physics: an Overview

02/02/2020 ∙ by Vladimir Krasnopolsky, et al. ∙ 0

In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameterizations, to ensure physical constraints, and control the accuracy of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 26

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.