Advancements in Neural ODEs

This chapter explores the intersection of neural networks and ordinary differential equations (ODEs), emphasizing recent research on scalable training methods for differential equations. The discussion highlights innovative strategies for leveraging existing numerical methods to improve the training of deep learning models, including insights from collaborations with the University of Toronto. It also covers the iterative development of neural architectures that enhance computational efficiency while modeling complex dynamics.