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We will focus on this latter problem here since we will deal with a good numerical approximation of experimental data based on spline quasi-interpolation to perform the integrals of the current and voltage as function of time.
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The resultant approximation of experimental data with the numerical model is shown in Fig. 2.
Hierarchical polynomial networks provide approximation of experimental data.
An approximation of experimental data by Langevin function is shown in the inset.
Moreover, we also unambiguously confirm the deterministic nature of the dynamics via numerical processing of experimental data.
Numerical simulations confirmed the relevance of experimental data.
The model was implemented in a numerical program, and the results were validated with the range of experimental data.
Once the analytical solution has been obtained, there is no need of further numerical solutions or simulations in order to analyze each particular set of experimental data.
This step helps to maintain a smoother approximation of the experimental data.
Approximation of the experimental data by (7) considering magnetic moment lognormal distribution gives much more precise results.
Numerical approximation of the long time behavior of a stochastic di.erential equation (SDE) is considered.
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