Tommaso Dorigo
(INFN, Padova)
28/09/2021, 10:00
Take the chain rule of differential calculus, model your system with continuous functions, add overparametrization and an effective way to navigate stochastically through the parameter space in search of an extremum of an utility function, and you have all it takes to find an optimal solution to even the hardest optimization problem. Deep learning, nowadays “differentiable programming”, is...
Dr
Srijit Paul
(Johannes Gutenberg University Mainz)
28/09/2021, 10:50
We discuss deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice. We elaborate on the applicability and limitations of this deep learning model in terms of extracting the relevant physics. The results are shown in context of 2D, 3D and 4D Ising, phi^4 and XY models.