Multi-scale variance reduction methods based on multiple control variates for kinetic equations with uncertainties
arXiv:1812.05485
Abstract
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.
arXiv admin note: text overlap with arXiv:1810.10844
References in corpus (1)
Cited by in corpus (8)
- Monte Carlo stochastic Galerkin methods for the Boltzmann equation with uncertainties: space-homogeneous case
- Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis
- Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo
- Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties
- Stochastic Galerkin particle methods for kinetic equations of plasmas with uncertainties
- A bi-fidelity stochastic collocation method for transport equations with diffusive scaling and multi-dimensional random inputs
- Uncertainty quantification for the BGK model of the Boltzmann equation using multilevel variance reduced Monte Carlo methods
- Monte Carlo stochastic Galerkin methods for non-Maxwellian kinetic models of multiagent systems with uncertainties