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Results for “uncertainty Quantification”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Unresolved resonance parameter evaluation and uncertainty quantification of n + 181Ta reactions
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Efficient polynomial chaos expansion for uncertainty quantification in power systems
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A generalized approximate control variate framework for multifidelity uncertainty quantification
We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling that accelerates the estimation of statistics of computationally expensive simulation models using an ensemble of models with lower cost. These lower cost models — which are typically lower fidelity with unknown statistics — are used to reduce the variance in statistical estimators relative to a MC estimator with equivalent cost. We derive the conditions under which our proposed approximate control variate framework recovers existing multifidelity variance reduction schemes as special cases. We demonstrate that existing recursive/nested strategies are suboptimal because they use the additional low-fidelity models only to efficiently estimate the unknown mean of the first low-fidelity model. As a result, they cannot achieve variance reduction beyond that of a control variate estimator that uses a single low-fidelity model with known mean. However, there often exists about an order-of-magnitude gap between the maximum achievable variance reduction using all low-fidelity models and that achieved by a single low-fidelity model with known mean. We show that our proposed approach can exploit this gap to achieve greater variance reduction by using non-recursive sampling schemes. The proposed strategy reduces the total cost of accurately estimating statistics, especially in cases where only low-fidelity simulation models are accessible for additional evaluations. Several analytic examples and an example with a hyperbolic PDE describing elastic wave propagation in heterogeneous media are used to illustrate the main features of the methodology.
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
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Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification
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Eigenvector continuation as an efficient and accurate emulator for uncertainty quantification
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Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
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Density and Thermal Expansivity of Molten 2LiF-BeF 2 (FLiBe): Measurements and Uncertainty Quantification
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Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search
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Evaluation of Sampling Algorithms Used for Bayesian Uncertainty Quantification of Molecular Dynamics Force Fields
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Uncertainty Quantification for Equation of State using Heterogeneous Data Sources
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Uncertainty Quantification for a TNT Equation of State Including the Kinetic Burn Model
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