Addressing nonlinearities in Monte Carlo.

ABSTRACT: Monte Carlo is famous for accepting model extensions and model refinements up to infinite dimension. However, this powerful incremental design is based on a premise which has severely limited its application so far: a state-variable can only be recursively defined as a function of underlying state-variables if this function is linear. Here we show that this premise can be alleviated by projecting nonlinearities onto a polynomial basis and increasing the configuration space dimension. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles, and concentrated solar power plant production, we prove the real-world usability of this advance in four test cases which were previously regarded as impracticab

SUBMITTER: Dauchet J 

PROVIDER: S-EPMC6125312 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature