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Recently, much attention has been devoted to these methods to construct analytic solutions; such as the perturbation method.
In the next proposition, we construct analytic solutions to the problem (70) with analytic forcing term and for vanishing initial data.
The Adomian decomposition method (ADM) is one of the most effective methods to construct analytic approximate solutions for nonlinear differential equations.
In this paper, based on the new definitions of the Adomian polynomials, and the double decomposition method, a new algorithm is proposed to construct analytic approximations for nonlinear differential equations with (coupled) boundary conditions.
In this paper, based on the new definition of the Adomian polynomials, Rach (2008) [22], the Adomian decomposition method and the Padé approximants technique, a new algorithm is proposed to construct analytic approximate solutions for nonlinear fractional differential equations with initial or boundary conditions.
Memos allow the researcher to capture comparisons and connections and to construct analytic notes [ 36].
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The Adomian decomposition method (ADM) is one of the most effective methods for constructing analytic approximate solutions.
The Adomian decomposition method (ADM) is one of the most effective methods for constructing analytic approximate solutions of nonlinear differential equations.
We construct an analytic parametrix, using the analytic pseudodifferential calculus in [195], that allows us to reconstruct the solenoidal part of a tensor field from its geodesic X-ray transform, up to a term that is analytic near Ω.
Readers should also bear in mind that results from different studies are often not directly comparable as they differ in the filters used to construct the analytic databases.
This method requires neither a priori knowledge on signals to construct any analytic dictionary, nor a large number of sample signals to form a training dictionary.
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