Lack of classical approximation functions (Weibull, exponential, Log-normal) for modeling the failure rate is that the modeled failure rate function [lambda] (t) in some cases of the experimental data has relatively poorly approximation of the experimental data.
Modeling the failure rate with the classical approximation functions and compared to a neural network model was built based on data from Table 1.
Thus, in a classical approximation there exists a correspondence between the minimisation of the 4-volume of the flow u(x, [tau]) on the cylinder and the minimisation of the 4-volume of the static flow defined in the Minkowski space by the vector field g(x), namely:
Let us consider some implications of our model for a real observer in a classical approximation (by the real observer we mean the reference frame of a topological feature).
In section 4 we discuss approximation methods based on classical approximation
theory, including Chebyshev and Newton polynomials, and related approaches.
The first, and most familiar, is the classical approximation ratio measure.
The classical approximation ratio analysis vastly increased our understanding of heuristics and optimization problems.
The main features of radiative [beta]-decay have been derived in the classical approximation
by Jackson  who assumes that an electron is created at the origin at t = 0 with constant velocity v = c[beta], in which case radiation of angular frequency [omega] is emitted in the direction of the unit vector n with an angular distribution in energy per unit time per unit interval of angular frequency
Just as in the classical approximation
theory, the weighted modulus of smoothness and the weighted K-functional are equivalent .
So we interpret the observed fractal 3-space as a classical approximation
to this "quantum foam".