  A numerically efficient inverse method for parametric model uncertainty
identification using maximum likelihood estimation is presented. The goal is to
identify a probability model for a fixed number of model parameters based on a
set of experiments. To perform maximum likelihood estimation, the output
probability density function is required. Forward propagation of input
uncertainty is established combining Polynomial Chaos and moment matching.
High-order moments of the output distribution are estimated using the
generalized Polynomial Chaos framework. Next, a maximum entropy parametric
distribution is matched with the estimated moments. This method is numerically
very attractive due to reduced forward sampling and deterministic nature of the
propagation strategy. The methodology is applied on a wet clutch system for
which certain model variables are considered as stochastic. The number of
required model simulations to achieve the same accuracy as the brute force
methodologies is decreased by one order of magnitude. The probability model
identified with the high order estimates resulted into a true log-likelihood
increase of about 4% since the accuracy of the estimated output probability
density function could be improved up to 47%.
