The 38th International Symposium on Lattice Field Theory

Machine Learning for Thermodynamic Observables

29 Jul 2021, 06:00

15m

Oral presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms (including Machine Learning, Quantum Computing, Tensor Networks)

Speaker

Kim Nicoli (Technische Universität Berlin)

Description

In this talk, I will discuss how thermodynamic observables of lattice field theories can be estimated using machine learning. Specifically¸ deep generative models are used to estimate the absolute value of the free energy. This is in contrast to MCMC-based methods which are limited to estimating differences of free energies. These methods come with the same asymptotic guarantees as the standard MCMC-based approaches. Application of these methods to two-dimensional $\phi^4$ theory will be presented and compared to existing approaches.

Presentation materials

167_R1Al_Kim_Nicoli

kim_nicoli_talk.pdf