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KIc and Wf results have been modeled by polynomial functions.

This paper extends the analytical redundancy techniques, well developed for linear systems, to FDI in non-linear dynamic systems modeled by polynomial differential algebraic equations.

The mean diameter and coefficient of variation were modeled by polynomial response surfaces as functions of solution concentration and voltage at each collector distance.

These subfunctions are modeled by polynomial chaos expansion (PCE) using moving least square technique which utilizes the benefits of orthogonality of the basis functions and provides adaptive interpolation between the support points.

This paper extends the analytic redundancy technique, widely developed for linear systems, to the FDI of non-linear dynamic systems modeled by polynomial differential algebraic equations.The existence of parity relations is briefly recalled and a definition of D-detectability is introduced.

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The dependence of the crack growth with respect to the uncertain parameters of Eq. (16) and with respect to the length of the initial cracks is modeled by polynomials (see Eq. (36)).

These parameters were modelled by polynomial fitting and reused in similar environments in the application phase.

As a result, statistical parameters are modelled by polynomial laws and can be calculated for any longitude.

Mild nonlinearities can to some degree be modelled by polynomial regression, using e.g. square- and interaction terms as extra regressors [ 12], but a robust metamodelling methodology must be capable also of handling strong nonlinearities, in particular non-monotone input-output relationships [ 13- 15].

The OLSE can represent the variability of the tropospheric process and can be modeled by a polynomial.

The temperature-dependent viscosities μ ⋆ λ ⋆, heat capacity c p ⋆ and heat conductivity k ⋆ can be modeled by standard polynomial laws, see, for example, [33, 35].

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