Incorporating Symmetry for Learning Spatiotemporal Dynamics
Rose Yu
University of California, San Diego (UCSD)
Computer Science and Engineering
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.
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