Rose discusses the integration of physics principles into machine learning to enhance the accuracy and reliability of models used in climate science. She highlights the challenges of traditional climate models and presents innovative deep learning approaches that emulate complex physical processes, such as turbulence in atmospheric dynamics. This research aims to provide timely predictions essential for understanding climate change and its impacts.
