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.
翻译:概览中引入了通用数学对象(绘图),并解释了其与模型物理参数化的关系;引入了可用于模拟和/或近似绘图的机器学习工具;应用ML以模拟现有参数化,开发新的参数化,确保物理限制,控制开发应用的准确性;介绍了允许开发者超越标准参数化范式的一些ML方法。