On multilevel Monte Carlo methods for deterministic and uncertain hyperbolic systems
نویسندگان
چکیده
In this paper, we evaluate the performance of multilevel Monte Carlo method (MLMC) for deterministic and uncertain hyperbolic systems, where randomness is introduced either in modeling parameters or approximation algorithms. MLMC a well known variance reduction widely used to accelerate (MC) sampling. However, demonstrate paper that whether can achieve real boost turns out be delicate. The computational costs MC depend on interplay among accuracy (bias) cost numerical single sample, as variances sampled corrections solutions. We characterize three regimes performances using those parameters, show may not even have higher when solutions are same order. Our studies carried by few prototype systems: linear scalar equation, Euler shallow water equations, relaxation model, above statements proved analytically some cases, demonstrated numerically cases stochastic equations driven white noise Glimm's random choice equations.
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ژورنال
عنوان ژورنال: Journal of Computational Physics
سال: 2023
ISSN: ['1090-2716', '0021-9991']
DOI: https://doi.org/10.1016/j.jcp.2022.111847