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Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates
We provide a collection of results on covariance expressions between Monte Carlo–based
multioutput mean, variance, and Sobol main effect variance estimators from an ensemble of …
multioutput mean, variance, and Sobol main effect variance estimators from an ensemble of …
Multifidelity covariance estimation via regression on the manifold of symmetric positive definite matrices
We introduce a multifidelity estimator of covariance matrices formulated as the solution to a
regression problem on the manifold of symmetric positive definite matrices. The estimator is …
regression problem on the manifold of symmetric positive definite matrices. The estimator is …
[HTML][HTML] Multi-level data assimilation for ocean forecasting using the shallow-water equations
Abstract Multi-level Monte Carlo methods have become an established technique in
uncertainty quantification as they provide the same statistical accuracy as traditional Monte …
uncertainty quantification as they provide the same statistical accuracy as traditional Monte …
Grouped approximate control variate estimators
This paper analyzes the approximate control variate (ACV) approach to multifidelity
uncertainty quantification in the case where weighted estimators are combined to form the …
uncertainty quantification in the case where weighted estimators are combined to form the …
Multilevel Monte Carlo methods for ensemble variational data assimilation
Ensemble variational data assimilation relies on ensembles of forecasts to estimate the
background error covariance matrix B. The ensemble can be provided by an Ensemble of …
background error covariance matrix B. The ensemble can be provided by an Ensemble of …
Multi-level data assimilation for simplified ocean models
Multi-level Monte Carlo methods have established as a tool in uncertainty quantification for
decreasing the computational costs while maintaining the same statistical accuracy as in …
decreasing the computational costs while maintaining the same statistical accuracy as in …