Multi-scale control variate methods for uncertainty quantification in kinetic equations

DOI10.1016/j.jcp.2019.03.002zbMath1452.65005arXiv1810.10844OpenAlexW2898508162MaRDI QIDQ2220598

Giacomo Dimarco, Lorenzo Pareschi

Publication date: 25 January 2021

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.10844

zbMATH Keywords

Monte Carlo methodskinetic equationsuncertainty quantificationmultiscale methodscontrol variatefluid-dynamic limitmultifidelity methods

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Stochastic analysis applied to problems in fluid mechanics (76M35) Rarefied gas flows, Boltzmann equation in fluid mechanics (76P05) Particle methods and lattice-gas methods (76M28)

Related Items (13)

On derivation and verification of a kinetic model for quantum vibrational energy of polyatomic gases in the gas-kinetic unified algorithm ⋮ 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 ⋮ A bi-fidelity method for the multiscale Boltzmann equation with random parameters ⋮ Multiscale Variance Reduction Methods Based on Multiple Control Variates for Kinetic Equations with Uncertainties ⋮ Lattice Boltzmann Method for Stochastic Convection-Diffusion Equations ⋮ 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 ⋮ Multivariate error modeling and uncertainty quantification using importance (re-)weighting for Monte Carlo simulations in particle transport ⋮ An Investigation of Uncertainty Propagation in Non-equilibrium Flows

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