We describe and analyze a Monte Carlo (MC) sampling framework for accelerating the estimation of statistics of computationally expensive simulation models using an ensemble of models with lower cost. Our approach uses control variates, with unknown means that must be estimated from data, to reduce the variance in statistical estimators relative to MC. Our framework unifies existing multi-level, multi-index, and multi-fidelity MC algorithms and leads to new and more efficient sampling schemes. Our results indicate that the variance reduction achieved by existing algorithms that explicitly or implicitly estimate control means, such as multilevel MC and multifidelity MC, is limited to that of a single linear control variate with known mean regardless of the number of control variates. We show how to circumvent this limitation and derive a new family of schemes that make full use of all available information sources. In particular, we demonstrate that a significant gap can exist, of orders of magnitude in some cases, between the variance reduction achievable by current We also present initial sample allocation approaches for exploiting this gap, which yield the greatest benefit when augmenting the high-fidelity model evaluations is impractical because, for instance, they arise from a legacy database. Several analytic examples and two PDE problems (viscous Burger's and steady state diffusion) are considered to demonstrate the methodology.
11/12/2018 ∙ by Alex A. Gorodetsky, et al. ∙ 0 ∙ share
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