This error norm is based on the mean error norm of the optimised CDF for each resampled (replicate) data series.
Assembled component models result in a 12% error norm at the system level.
We also provide the error norm with respect to the wavelength of the propagated wave-packet.
The squared displacement error norm of a designated evaluation point is used as the objective function and is minimized under an area constraint.
This problem is formulated as a parameter-free shape optimization problem, in which a squared displacements error norm on the prescribed region is employed as an objective functional.
In particular, we demonstrate how an ensemble error norm can be used to select the most appropriate extreme probability model from a choice of cumulative distribution functions (CDFs).
The appropriate input parameters are determined based on the error norm in order to increase the reliability of the proposed relationship.
Based on theory of least squares and by extreme value analysis of the error norm, detailed derivations of the model and the main formulas are obtained.
The identification problem is then formulated as an optimization program in which the error norm of the equilibrium equation is minimized.
Further, inertia-based asymmetry is studied and an analytical method is proposed (L1 error norm = 0.05) to understand the deflection observed in the plane of sheet formation.
The PODI calculations are completed within 1.25 s on a normal desktop machine with the relative ℓ2 error norm not exceeding 2.5×10−3.
