Rose discusses innovative methods for integrating physical knowledge into deep learning models, particularly through convolutional operators that approximate solutions to partial differential equations. By leveraging symmetries inherent in these equations, she explains how incorporating these principles can enhance predictive accuracy and ensure conservation laws are respected in physical modeling. This approach opens new avenues for modeling complex systems like turbulence more effectively.
