Jul 6 – 8, 2021
Europe/Zurich timezone

Learning Symmetries and Conserved Quantities of Physical Systems

Jul 8, 2021, 9:00 AM
20m

Speaker

Sven Krippendorf

Description

This talk is about how we can use ML to identify symmetries (conserved quantities) of physical systems. I report on three different strategies to find symmetries:
1) By examining the embedding a (deep) neural network adapts on a simple supervised task (2003.13679).
2) By imposing a modification to Hamiltonian Neural Networks such that a coordinate transformation ensures the emergence of conserved quantities (symmetry control neural networks, 2104.14444).
3) By searching for a Lax pair/connection to identify whether a system is integrable (2103.07475), i.e. it has as many conserved quantities as degrees of freedom.
I comment on how strategies 1) and 3) enable us to search for new mathematical structures and how 2) can be used to accelerate simulations.

Affiliation LMU Munich

Primary author

Presentation materials