PIRSA:24110078 Scientific Series

DOI10.48660/24110078

Series: Machine Learning Initiative

Ihssen, F. (2024). Physics-Informed Renormalization Group Flows. Perimeter Institute. https://pirsa.org/24110078

Ihssen, Friederike. Physics-Informed Renormalization Group Flows. Perimeter Institute, Nov. 22, 2024, https://pirsa.org/24110078 

@misc{ pirsa_PIRSA:24110078,
  doi = {10.48660/24110078},
  url = {https://pirsa.org/24110078},
  author = {Ihssen, Friederike},
  keywords = {},
  language = {en},
  title = {Physics-Informed Renormalization Group Flows},
  publisher = {Perimeter Institute},
  year = {2024},
  month = {nov},
  note = {PIRSA:24110078 see, \url{https://pirsa.org}}
}

Abstract

The physics of strongly correlated systems offers some of the most intriguing physics challenges such as competing orders or the emergence of dynamical composite degrees of freedom. Often, the resolution of these physics challenges is computationally hard, but can be simplified by a formulation in terms of the appropriate dynamical degrees of freedom.

Resources

Next talk