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In addition, we provide numerical simulations of two-dimensional Rayleigh Bénard convection and periodic forced flow.
We also provide numerical simulations which demonstrate the convergence results and underline the effect of the time varying high gain estimator.
Finally, we provide numerical simulations to assess the performance of the proposed algorithms, the validity of the derived ARL expression, and to reveal the ARL's insightful properties.
We also provide numerical simulations which demonstrate the convergence results and underline the effect of the time varying gain estimator on peaking value reduction.
Finally, using the splitting operator approach, and well-suited numerical methods for each operator, we provide numerical simulations for mosquito spreading, and test different vector control scenarios.
Last but not least, we adopt the Chebychev collocation method, that uses backward Euler method and the Gauss-Lobatto points, to provide numerical simulations in order to ascertain the correctness of the theoretical outcomes.
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We provide numerical simulation to support our analysis, and we also draw practical conclusions for machine design.
And we provide numerical simulation results to validate the performance of our method using real backbone network traffic data.
Standard Galerkin Finite Element Method is used to provide numerical simulation of solidification phenomenon in the present study and Adaptive Grid Refinement is employed to solve the governing equations.
In this section, we provide numerical simulation results to demonstrate the mathematical analysis conclusions.
In this section, we provide numerical simulation results to substantiate the analytical findings for the stochastic model system reported in the previous sections.
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