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i- flow: High-dimensional integration and sampling with normalizing flows

Gao, Christina·Isaacson, Joshua·Krause, Claudius
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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.

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDSMonte CarloMonte Carlo integrationdensity estimationimportance samplingnormalizing flowsrandom number generators

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Gao, Christina, Isaacson, Joshua, Krause, Claudius. 2020-11-18. i- flow: High-dimensional integration and sampling with normalizing flows. https://doi.org/10.1088/2632-2153%2Fabab62

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