1 research outputs found
Tensor Krylov methods for model reduction of the stochastic mean of a parametric dynamical system
Uncertainties in mathematical models are often
represented by stochastic parameters. We consider high dimen-
sional single-input single-output (SISO) systems whose system
matrices have affine dependencies on stochastically uncorre-
lated parameters. We introduce a reformulated SISO system
for the mean of the stochastic output of the original parametric
system. The problem is reformulated using tensors, represented
in low-rank format. A two-sided tensor Arnoldi method is used
for model order reduction of the high dimensional formulation.
This results in a reduced model for the mean that is compared
to the parametric reduced model that results from classical
parametric model reductionstatus: publishe