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i- flow: High-dimensional integration and sampling with normalizing flows
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DOI：10.1088/2632-2153/abab62.png)
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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.
Keyword:
normalizing flows
Monte Carlo integration
importance sampling
random number generators
Monte Carlo
density estimation
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