Solving physics many-body problems with deep learning

PIRSA ID: 19110081
Event Type: Seminar
Domaine(s) scientifique(s) :
Quantum Matter
,
Quantum Information
,
Other
Date de fin :
Speaker(s):
  • Frank Noe, Freie Universität Berlin

Solving classical and quantum physics many-body systems are amongst the hardest problems in the natural sciences, but also of fundamental importance for applications such as material and drug design. In this talk, I will give a an overview of fundamental physics problems at multiple time- and lengthscales and describe deep learning methods to address them: 1) solving the quantum-chemical electronic Schrödinger equation with deep variational Monte Carlo, 2) learning to coarse-grain many-body systems, and 3) sampling equilibrium states of classical many-body systems with generative learning.