Multiscale Variance Reduction Methods Based on Multiple Control Variates for Kinetic Equations with Uncertainties

DOI10.1137/18M1231985zbMath1433.76150arXiv1812.05485MaRDI QIDQ5222125

Giacomo Dimarco, Lorenzo Pareschi

Publication date: 31 March 2020

Published in: Multiscale Modeling & Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.05485

zbMATH Keywords

Monte Carlo methodskinetic equationsuncertainty quantificationmultiscale methodsmultifidelity methodsmultiple control variates

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Rarefied gas flows, Boltzmann equation in fluid mechanics (76P05) Particle methods and lattice-gas methods (76M28) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)

Related Items (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 ⋮ Monte Carlo stochastic Galerkin methods for non-Maxwellian kinetic models of multiagent systems with uncertainties ⋮ A bi-fidelity stochastic collocation method for transport equations with diffusive scaling and multi-dimensional random inputs ⋮ Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization ⋮ Stochastic Galerkin particle methods for kinetic equations of plasmas with uncertainties ⋮ Uncertainty Quantification for the BGK Model of the Boltzmann Equation Using Multilevel Variance Reduced Monte Carlo Methods ⋮ Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties

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