Polynomial chaos with high-order moment matching estimates output densities from 25 simulations per experiment, and maximum entropy fits improve the identified wet clutch log-likelihood by about 4% over Gaussian fits.
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Inverse Parametric Uncertain Identification using Polynomial Chaos and high-order Moment Matching benchmarked on a Wet Friction Clutch
Polynomial chaos with high-order moment matching estimates output densities from 25 simulations per experiment, and maximum entropy fits improve the identified wet clutch log-likelihood by about 4% over Gaussian fits.