Abstract
Within analysis of dynamical systems embracing uncertain impacts the output can be generally viewed as a function defined in a random domain with dependence on time or frequency. Without loss of generality, a function defined on the normalized random domain, i.e., a unit hypercube, is considered where the sensitivity analysis plays a key role in many issues, e.g. uncertainty reduction, model simplification, exploration of significant random parameters, etc. Variance-based global sensitivity indices provide adequate estimates for the influence of random variables and become one of the most powerful instruments in sensitivity analysis. Alternatively, if the function is differentiable, the derivative-based sensitivity measures have received much attention due to lower computational costs. We introduce numerical strategies for computing derivative-based sensitivity indices in the case of high-dimensional hypercubes and present numerical simulations of a test example which models the linear electric circuit of a band-stop filter.
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Liu, Q., Pulch, R. (2018). Numerical Methods for Derivative-Based Global Sensitivity Analysis in High Dimensions. In: Langer, U., Amrhein, W., Zulehner, W. (eds) Scientific Computing in Electrical Engineering. Mathematics in Industry(), vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-75538-0_15
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DOI: https://doi.org/10.1007/978-3-319-75538-0_15
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