PDE och tillämpningar: Neural networks for quantum many-body physics

Recently, it has been demonstrated that certain algorithms of machine learning can be extremely efficient for solving many-body quantum models in the regimes, where the conventional methods, like QMC or exact diagonalization, are barely applicable. I will briefly review the main ideas behind this approach, discuss the current state of affairs in this field, and outline important challenges.