Multi-scale variance reduction methods based on multiple control variates for kinetic equations with uncertainties
This work addresses the need for efficient uncertainty quantification in high-dimensional kinetic equations, offering an incremental improvement to existing variance reduction techniques.
The authors generalize multiscale control variate methods to use multiple control variates for kinetic equations with uncertainties, achieving improved variance reduction over single-control-variate approaches.
The development of efficient numerical methods for kinetic equations with stochastic parameters is a challenge due to the high dimensionality of the problem. Recently we introduced a multiscale control variate strategy which is capable to accelerate considerably the slow convergence of standard Monte Carlo methods for uncertainty quantification. Here we generalize this class of methods to the case of multiple control variates. We show that the additional degrees of freedom can be used to improve further the variance reduction properties of multiscale control variate methods.