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The ability to estimate various sources of numerical error and to adaptively control them is a powerful tool in quantifying uncertainty in predictive simulations. This work attempts to develop reliable estimates of numerical errors resulting from spatial, temporal and stochastic approximations of fluid dynamic equations using a discrete adjoint approach. Each source of error is isolated and the accuracy...
We propose a framework based on the use of adjoint equations to formulate an adaptive sampling strategy for uncertainty quantification for problems governed by algebraic or differential equations involving random parameters. The approach is non-intrusive and makes use of discrete sampling based on collocation on simplex elements in stochastic space. Adjoint or dual equations are introduced to estimate...
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