  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.
