i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Abstract
In many fields of science, high-dimensional integration is required. Numerical methods have been developed to evaluate these complex integrals. We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows. Normalizing flows are machine-learned, bijective mappings between two distributions. i-flow can also be used to sample random points according to complicated distributions in high dimensions. We compare i-flow to other algorithms for high-dimensional numerical integration and show that i-flow outperforms them for high dimensional correlated integrals. The i-flow code is publicly available on gitlab at https://gitlab.com/i-flow/i-flow.
- Publication:
-
arXiv e-prints
- Pub Date:
- January 2020
- DOI:
- 10.48550/arXiv.2001.05486
- arXiv:
- arXiv:2001.05486
- Bibcode:
- 2020arXiv200105486G
- Keywords:
-
- Physics - Computational Physics;
- Computer Science - Machine Learning;
- High Energy Physics - Phenomenology;
- Statistics - Machine Learning
- E-Print:
- 21 pages, 5 figures, 4 tables