Description
Chair: Julia Kempe
The laws of physics obey exact symmetries. Equivariant machine learning (ML) approaches aim to encode these symmetries in our models, which is important for ensuring our astrophysical analyses are physically motivated as well as improving performance. In this talk, I will outline the physical symmetries we care about, current equivariant ML techniques, and relevant astrophysical and cosmological problems. I will present a recently developed approach based on invariant scalars and its application to characterizing the dark matter distribution in cosmological simulations, and discuss future directions and opportunities.
