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i-flow: High-dimensional Integration and Sampling with Normalizing Flows

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arxiv 2001.05486 v2 pith:6OKZC7GN submitted 2020-01-15 physics.comp-ph cs.LGhep-phstat.ML

Christina Gao , Joshua Isaacson , Claudius Krause This is my paper · ORCID
classification physics.comp-phcs.LGhep-phstat.ML
keywords i-flowhigh-dimensionalintegrationflowsnormalizingnumericalcodedistributions
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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