PIRSA:19110081 Scientific Series Condensed Matter Quantum Information Other

DOI10.48660/19110081

Series: Machine Learning Initiative

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

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

@misc{ pirsa_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}}
}

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.

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