Description
The 40th Seminar
Date and Time: Nov. 1st 2:00 pm – 3:00 pm(JST)
Venue: Zoom webinar
Language: English
Speaker: Shinichi Nakajima (Technical University Berlin)
Title: Monte Carlo simulation of physical systems with deep generative models
Short Abstract:
Deep learning has shown its usefulness in many fields of science. In this talk, we introduce our recent processes in Monte Carlo simulation in physics with deep generative models. Specifically, we extend our previous work, where we used generative models, e.g., autoregressive models and normalizing flows, that not only allow efficient sample generation but also provide the exact normalized sampling probability, and applied neural importance sampling for obtaining unbiased estimators. Our extension includes its application to estimating thermodynamic observables, such as free energy and entropy, in the lattice field theory, detection and mitigation of mode dropping, which can violates the condition for unbiasedness, and efficient training with path gradient estimators. Our experiments showed that our approaches can enhance the utility of deep generative models in physics, improve its reliability, and reduce the computational cost.
