Physics Informed Machine Learning in Astronomy


Machine Learning had a significant success in Astronomy in recent years, and it becomes obvious that useful applications of ML require a tight connection to physical modelling. In this session, we will explore several aspects of imbuing physics as part of a ML model, from building hybrid models that merge both deep learning and physical models, to using known physical symmetries and equivariances to design dedicated neural architectures.

Organisers: Francois Lanusse (Chair) & Jean-Luc Starck (Committee member)

Session Schedule

17:30 - 17:50 Invited talk
Francois Lanusse
17:50 - 18:10 Invited talk
Laurence Perreault-Levasseur
18:10 - 18:30 Invited talk
Aymeric Galan
18:30 - 18:50 Invited talk
Tobias Liaudat
18:50 - 19:10 Invited talk
Kate Storey-Fisher
19:10 - 20:30 Invited poster
Benjamin Remy
19:10 - 20:30 Contributed poster
Utsav Akhaury
19:10 - 20:30 Contributed poster
Jason McEwen
19:10 - 20:30 Contributed poster
Speaker: Awaiting decision from call for abstracts