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Gradient-based design optimization based on adjoint method for sensitivity analysis offers a feasible design approach.
Based on the combined normalization, a method for sensitivity analysis of eigenvalues and eigenvectors is studied.
The algorithm applies a new method for sensitivity calculations of the failure probability.
Combining the developed model with feedback control theory also provides a novel method for sensitivity analysis.
In order to demonstrate the capability of the proposed method for sensitivity analysis, several simulations are conducted.
This paper presents an explicit time-domain method for sensitivity analysis of structural responses under non-stationary random excitations.
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This makes computationally efficient methods for sensitivity analysis, uncertainty quantification and probabilistic risk assessment indispensable.
Traditional methods for sensitivity analysis of the model with scalar output fail to provide satisfactory results for this multivariate case.
Computational methods for sensitivity analysis are invaluable tools for scientists and engineers investigating a wide range of physical phenomena.
Methods for sensitivity analysis to assess the impact of bias in the efficacy subset analysis are described.
We introduce a broad range of methods for sensitivity analysis, including the brute-force finite difference, popular semianalytical methods, and mathematically rigorous and accurate continuum-based approaches.
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