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• W3000095629 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." @default.
• W3000095629 created "2020-01-23" @default.
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• W3000095629 date "2020-11-18" @default.
• W3000095629 modified "2023-10-16" @default.
• W3000095629 title "<tt>i- flow</tt>: High-dimensional integration and sampling with normalizing flows" @default.
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• W3000095629 doi "https://doi.org/10.1088/2632-2153/abab62" @default.
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