Toward Scalable Normalizing Flows for the Hubbard Model
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910000934289408 |
|---|---|
| author | Kreit, Janik Bulgarelli, Andrea Funcke, Lena Luu, Thomas Schuh, Dominic Singh, Simran Verzichelli, Lorenzo |
| author_facet | Kreit, Janik Bulgarelli, Andrea Funcke, Lena Luu, Thomas Schuh, Dominic Singh, Simran Verzichelli, Lorenzo |
| contents | Normalizing flows have recently demonstrated the ability to learn the Boltzmann distribution of the Hubbard model, opening new avenues for generative modeling in condensed matter physics. In this work, we investigate the steps required to extend such simulations to larger lattice sizes and lower temperatures, with a focus on enhancing stability and efficiency. Additionally, we present the scaling behavior of stochastic normalizing flows and non-equilibrium Markov chain Monte Carlo methods for this fermionic system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18273 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Scalable Normalizing Flows for the Hubbard Model Kreit, Janik Bulgarelli, Andrea Funcke, Lena Luu, Thomas Schuh, Dominic Singh, Simran Verzichelli, Lorenzo Strongly Correlated Electrons Machine Learning High Energy Physics - Lattice Normalizing flows have recently demonstrated the ability to learn the Boltzmann distribution of the Hubbard model, opening new avenues for generative modeling in condensed matter physics. In this work, we investigate the steps required to extend such simulations to larger lattice sizes and lower temperatures, with a focus on enhancing stability and efficiency. Additionally, we present the scaling behavior of stochastic normalizing flows and non-equilibrium Markov chain Monte Carlo methods for this fermionic system. |
| title | Toward Scalable Normalizing Flows for the Hubbard Model |
| topic | Strongly Correlated Electrons Machine Learning High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2601.18273 |