Speaker: Morten Hjorth-Jensen
Title: Many-body methods, from variational ones to machine learning approaches'
The main aim is to give you a short and pedestrian introduction to how we can use Machine Learning methods to solve quantum mechanical many-body problems. And why this could be of interest. I will focus on the link between variational methods (Variational Monte Carlo as an example) and so-called Boltzmann machines and how they can be used to solve many-body problems. This work is much inspired by the recent article of G. Carleo and M. Troyer, Science 355, Issue 6325, pp. 602-606 (2017).
Morten is a professor of Physics at the University of Oslo, with a strong interest in computational physics and many-body theory in general, and the nuclear many-body problem and nuclear structure problems in particular. He shares his time equally between the University of Oslo and Michigan State University, and has been the supervisor of many students and former students at the Hylleraas Centre.
The Hylleraas seminars alternate between Oslo (room V205) and Tromsø (room C302), and are broadcasted by video conference to the other place. It is possible to follow the seminars externally upon request.