On the solutions of some fractional q-differential equations with the Riemann-Liouville fractional q-derivative

This paper is devoted to explicit and numerical solutions to linear fractional q-difference equations and the Cauchy type problem associated with the Riemann-Liouville fractional q-derivative in q-calculus. The approaches based on the reduction to Volterra q-integral equations, on compositional relations, and on operational calculus are presented to give explicit solutions to linear q-difference equations. For simplicity, we give results involving fractional q-difference equations of real order a > 0 and given real numbers in q-calculus. Numerical treatment of fractional q-difference equations is also investigated. Finally, some examples are provided to illustrate our main results in each subsection.

Efficient implementation of advanced Richardson Extrapolation in an atmospheric chemical scheme

The numerical treatment of an atmospheric chemical scheme, which contains 56 species, is discussed in this paper. This scheme is often used in studies of air pollution levels in different domains, as, for example, in Europe, by large-scale environmental models containing additionally two other important physical processes—transport of pollutants in the atmosphere (advection) and diffusion phenomena. We shall concentrate our attention on the efficient numerical treatment of the chemical scheme by using Implicit Runge–Kutta Methods combined with accurate and efficient advanced versions of the Richardson Extrapolation. A Variable Stepsize Variable Formula Method is developed in order to achieve high accuracy of the calculated results within a reasonable computational time. Reliable estimations of the computational errors when the proposed numerical methods are used in the treatment of the chemical scheme will be demonstrated by presenting results from several representative runs and comparing these results with “exact” concentrations obtained by applying a very small stepsize during the computations. Results related to the diurnal variations of some of the chemical species will also be presented. The approach used in this paper does not depend on the particular chemical scheme and can easily be applied when other atmospheric chemical schemes are selected.

Application of Asymptotic Homotopy Perturbation Method to Fractional Order Partial Differential Equation

In this article, we introduce a new algorithm-based scheme titled asymptotic homotopy perturbation method (AHPM) for simulation purposes of non-linear and linear differential equations of non-integer and integer orders. AHPM is extended for numerical treatment to the approximate solution of one of the important fractional-order two-dimensional Helmholtz equations and some of its cases . For probation and illustrative purposes, we have compared the AHPM solutions to the solutions from another existing method as well as the exact solutions of the considered problems. Moreover, it is observed that the symmetry or asymmetry of the solution of considered problems is invariant under the homotopy definition. Error estimates for solutions are also provided. The approximate solutions of AHPM are tabulated and plotted, which indicates that AHPM is effective and explicit.

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, susceptible/infected populations of host plant, susceptible/infected vectors population, and population of predator. The numerical performances through the LMBNN solver are observed for three different types of the nonlinear host-vector-predator model using the authentication, testing, sample data, and training. The proportions of these data are chosen as a larger part, i.e., 80% for training and 10% for validation and testing, respectively. The nonlinear host-vector-predator model is numerically treated through the LMBNNs, and comparative investigations have been performed using the reference solutions. The obtained results of the model are presented using the LMBNNs to reduce the mean square error (MSE). For the competence, exactness, consistency, and efficacy of the LMBNNs, the numerical results using the proportional measures through the MSE, error histograms (EHs), and regression/correlation are performed.