Sentence examples for analysis uncertainty quantification from inspiring English sources
This makes computationally efficient methods for sensitivity analysis, uncertainty quantification and probabilistic risk assessment indispensable.
For this reason it becomes practically impossible to perform a robust and comprehensive analysis that requires multiple simulations such as sensitivity analysis, uncertainty quantification and optimization.
By using these sub-systems, MARBLE covers all phases required in fast reactor core design prediction and improvement procedures, i.e. integral experiment database management, nuclear data processing, fast criticality assembly analysis, uncertainty quantification, and power reactor analysis.
SHARKX is a set of Perl-based tools build around the lattice code CASMO-5, used to perform sensitivity analysis (SA), uncertainty quantification (UQ) and representativity analysis (RA) developed at the Paul Scherrer Institut.
We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification.
Typical uses of surrogate models include design, optimization, sensitivity analysis and/or uncertainty quantification.
The probabilistic analysis or uncertainty quantification is an essential part of verification and validation (V&V) and is used to provide confidence in model predictions.
This paper deals with the dynamical analysis and uncertainty quantification of a mistuned industrial rotating integrally bladed disk, for which the operating regime under consideration takes into account the nonlinear geometrical effects induced by large displacements and deformations.
Then, the presented modeling approach is rigorously verified and validated through simulations of the bi-material composite structures' manufacturing processes, including a mesh convergence study, sensitivity analysis, and uncertainty quantification.
The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables.
Each of the astronomical subfields could benefit from improved time series analysis, hierarchical modeling, uncertainty quantification, reduced order modeling and inference with misspecified models and will be addressed.
