Sentence examples for method of uncertainty from inspiring English sources

The method of uncertainty minimization using polynomial chaos expansions (MUM-PCE) (Sheen and Wang, 2011) was employed to constrain the model uncertainty for laminar flame speed predictions.

In this work, we propose the Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) to quantify and constrain these uncertainties.

It is shown that the accuracy of the reduced models can be improved by optimization through the method of uncertainty minimization by polynomial chaos expansion (MUM-PCE).

They found that these trends are statistically significant at the 95% level, and that a sophisticated method of uncertainty estimation must be used because simple classical methods underestimate the uncertainty.

In this paper the efficacy of an approximate method of uncertainty propagation, known as the first-order second-moment (FOSM) method, for use in seismic loss estimation is investigated.

The final mechanism is determined based on the method of uncertainty minimization using polynomial chaos expansions by comparing the predicted uncertainty of the optimized mechanisms with available experimental data in shock tubes, rapid compression machines, and jet-stirred reactors.

In this paper, reliability analysis of slope stability is presented using two methods of uncertainty first-order second-moment method (FOSM) and Monte Carlo simulation method (MCSM).

The treatment of this problem calls for combining methods of uncertainty analysis with finite element structural modeling and numerical optimization tools.

The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.

Targeted towards the continuous support of the construction process, a concept for real-time steering support of tunnel boring machines in conjunction with model update procedures and methods of uncertainty quantification is addressed.

In this paper, we combine laboratory experiments performed at the University of Innsbruck with current methods of uncertainty and sensitivity analysis for assessing adequacy, predictive power and robustness of the models.