Recorded 26 January 2023. Rose Yu of the University of California, San Diego, presents "Incorporating Symmetry for Learning Spatiotemporal Dynamics" at IPAM's Learning and Emergence in Molecular Systems Workshop. Abstract: While deep learning has shown tremendous success in many scientific domains, it remains a grand challenge to incorporate physical principles into such models. In physics, Noether’s Law gives a correspondence between conserved quantities and groups of symmetries. By building a neural network that inherently respects a given symmetry, we thus make conservation of the associated quantity more likely and consequently the model’s prediction more physically accurate. In this talk, I will demonstrate how to incorporate symmetries into deep neural networks and significantly improve physical consistency, sample efficiency, and generalization in learning spatiotemporal dynamics. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/learning-and-emergence-in-molecular-systems/