PIRSA:19110081

Solving physics many-body problems with deep learning

APA

Noe, F. (2019). Solving physics many-body problems with deep learning. Perimeter Institute. https://pirsa.org/19110081

MLA

Noe, Frank. Solving physics many-body problems with deep learning. Perimeter Institute, Nov. 12, 2019, https://pirsa.org/19110081

BibTex

          @misc{ pirsa_19110081,
            doi = {10.48660/19110081},
            url = {https://pirsa.org/19110081},
            author = {Noe, Frank},
            keywords = {Condensed Matter, Quantum Information, Other},
            language = {en},
            title = {Solving physics many-body problems with deep learning},
            publisher = {Perimeter Institute},
            year = {2019},
            month = {nov},
            note = {PIRSA:19110081 see, \url{https://pirsa.org}}
          }
          

Frank Noe Freie Universität Berlin

Abstract

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.