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The explosion of spatiotemporal data in the physical world requires new deep-learning tools to model complex dynamical systems. On the other hand, dynamical system theory plays a key role in understanding the emerging behavior of deep neural networks.  In this talk, I will give an overview of our research to explore the interplay between the two. I will showcase the applications of these approaches in fluid mechanics, autonomous driving, and optimization.