A machine learning approach for efficient multi-dimensional integration.

ABSTRACT: Many physics problems involve integration in multi-dimensional space whose analytic solution is not available. The integrals can be evaluated using numerical integration methods, but it requires a large computational cost in some cases, so an efficient algorithm plays an important role in solving the physics problems. We propose a novel numerical multi-dimensional integration algorithm using machine learning (ML). After training a ML regression model to mimic a target integrand, the regression model is used to evaluate an approximation of the integral. Then, the difference between the approximation and the true answer is calculated to correct the bias in the approximation of the integral induced by ML prediction errors. Because of the bias correction, the final estimate of the integral is

SUBMITTER: Yoon B 

PROVIDER: S-EPMC8460840 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature