Data-Driven Arbitrary Polynomial Chaos for Uncertainty Quantification in Filters
Keywords:
Data-driven arbitrary polynomial chaos, generalized polynomial chaos, Monte Carlo sampling, uncertainty quantificationAbstract
A non-intrusive arbitrary polynomial chaos (aPC) method is applied to a problem of a band-stop filter with geometrical imperfections. The construction of aPC scheme only requires evaluating a finite number of moments, and does not involve assigning analytical probability density functions for the uncertain parameters of a stochastic model. Therefore, aPC is well suited for applications where the uncertain parameters are represented by raw data samples, as with the case of experimental measurements. The numerical examples show that the aPC approach is accurate even with a limited number of input samples.
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References
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