Generative Models for Statistical Field Theories

In the final talk of the DEEP-IN series, we will explore the role of generative models in learning phase transitions and sampling in lattice systems. First, we demonstrate how generative models can serve as global samplers by learning the underlying probability distributions. This enables the sampling of configurations more efficiently for lattice field theories. We will also demonstrate how the ferromagnetic phase transition, the Kosterlitz-Thouless transition, and quantum phase transitions can be identified from generative models. I will briefly introduce generative diffusion models, which can be interpreted as a stochastic quantization scheme. This opens a new path for understanding deep generative models.

This is an informal seminar, we will start with the methodology and some practical examples, and finally reserve time for everyone interested to discuss it together.