Machine Learning at the Flatiron Institute Seminar: Sebastian J. Wetzel

Date & Time

Title: Finding Symmetry Invariants and Conserved Quantities with Artificial Neural Networks

Abstract: In this talk, I will discuss how to find symmetry invariants and conserved quantities with artificial neural networks. This topic falls within the now-emerging subfield of artificial scientific discovery. More precisely, the method is based on interpreting what artificial neural networks learn when trained on data from systems in theoretical physics in order to reveal physical properties. The central method described in this talk is based on Siamese Neural Networks. However, I will also discuss recent developments of methods that are more accurate and allow for learning a full set of independent conserved quantities.

About the Speaker

Sebastian studied physics and applied mathematics at the University of Heidelberg and the University of Cambridge. He is now a Research Scientist at the University of Waterloo and Perimeter Institute. His main research interest is artificial intelligence in the physical sciences where his most important contributions are related to using machine learning to calculate phase diagrams and artificial scientific discovery through the interpretation of neural networks.

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