The use of machine learning techniques in problems related to astronomy has grown exponentially in the last decade. Nowadays applications do not only “limit” themselves to extract relevant information from large datasets and/or optimize observations/survey strategies, but also to solve complex equations and “efficiently store” multi-parametric models.
In this mini-workshop (two days of ~3 synchronous hours each), speakers will focus on presenting practical cases of use of “classical” techniques as well as deep learning and also physics informed methods. We will conclude each day with a ~1 hour round table discussion on challenges ahead of the different topics.
Registration will be free of charge but limited to ensure participation in the Q&A and round table discussions.
The workshop is open to participants with basic / advanced knowledge of machine learning as well as to beginners with interest in knowing more about these techniques. Practical experience will be positively received.
Deadline for applications: 26th July
Mini workshop “Machine Learning in Astronomy, from classical to physics-informed”
Date: 28-29 July 2021 (TBC)
Organizers: A. Bayo (UV), S. Bovino (UdeC), F. Forster (UCh), T. Grassi (MPE)
Sponsored by MAS, NPF, TITANs, MPE
Speakers: K. Peña, M. Araya, G. Cabrera, P. Sanchez-Saez, M. Matthaiakis, T. Grassi, A. Ribas