Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science

Mode, Charles J.

doi:10.5772/634

Cited by 24 publications

(1 citation statement)

References 5 publications

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“…Often high-dimensional phase space integrals with non-trivial correlations between dimensions are required in important theory calculations. Monte-Carlo (MC) methods still remain as the most important techniques for solving high-dimensional problems across many fields, including for instance: biology [1,2], chemistry [3], astronomy [4], medical physics [5], finance [6] and image rendering [7]. In high-energy physics, all analyses at the Large Hadron Collider (LHC) rely strongly on multipurpose Monte Carlo event generators [8,9] for signal or background prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Gao,

Isaacson,

Krause

2020

Preprint

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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.

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