Giacomo Dimarco, Lorenzo Pareschi(Multiscale Modeling & Simulation, 18(1), 351–382, 2020, arXiv:1812.05485, )
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.
We show that the additional degrees of freedom can be used to improve further the variance reduction properties of multiscale control variate methods.