2019
DOI: 10.1080/16583655.2019.1618547
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Reproducing kernel Hilbert space method for the solutions of generalized Kuramoto–Sivashinsky equation

Abstract: Reproducing kernel Hilbert space method is given for the solution of generalized Kuramoto-Sivashinsky equation. Reproducing kernel functions are obtained to get the solution of the generalized Kuramoto-Sivashinsky equation. Two examples have been introduced to prove the accuracy of the method. The obtained results show that the reproducing kernel Hilbert space method gives approximate analytical solutions which are very close to the exact solution of the generalized Kuramoto-Sivashinsky equation, which demonst… Show more

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Cited by 12 publications
(9 citation statements)
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“…Besides calculating the time-evolution of the wave function and the way to minimize the cost function we used as described in ref. [30], there are other mathematical ways about fractional derivatives to calculate differential equations [36][37][38][39][40][41][42][43][44][45]. Figure 3(a) shows the atomic wave function as time when the dimple-ring potential is shaken, resulting in the optimized control to excite the ground state of the dimplering trap for 20 ms and hold the excited state in the dimplering trap for 20 ms. Figure 3(b) shows the optimized control in black and the initial guess in red.…”
Section: ∫ ( ( )mentioning
confidence: 99%
“…Besides calculating the time-evolution of the wave function and the way to minimize the cost function we used as described in ref. [30], there are other mathematical ways about fractional derivatives to calculate differential equations [36][37][38][39][40][41][42][43][44][45]. Figure 3(a) shows the atomic wave function as time when the dimple-ring potential is shaken, resulting in the optimized control to excite the ground state of the dimplering trap for 20 ms and hold the excited state in the dimplering trap for 20 ms. Figure 3(b) shows the optimized control in black and the initial guess in red.…”
Section: ∫ ( ( )mentioning
confidence: 99%
“…Therefore, there has been significant progress in the development of diverse schemes for treating NLSEs and nonlinear partial differential equations (NPDEs) in the general case. For approximate schemes, we cite the Adomian decomposition method [5,6], collocation method [7], homotopy perturbation method [8], homotopy analysis method [9], reduced differential transform method [10,11], q-homotopy analysis method [12], variational iteration method [13], reproducing kernel Hilbert space method [14], iterative Shehu transform method [15], and residual power series method [16,17]. While constructing an exact analytic solution is of more importance since this can provide the best understanding of the model's nature to be processed in an efficient way, researchers have developed various powerful tools to analyze NPDEs.…”
Section: Introductionmentioning
confidence: 99%
“…Various other mathematical systems can be obtained in research areas including computational biology, medicine, microbiology, biophysics, physiology, environmental science, and many more. Classical systems modeled with ordinary derivatives have been investigated under operators from fractional calculus [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. These fractional operators called Riemann--Liouville, Caputo, Caputo-Fabrizio, Atangana-Baleanu, Atangana-Gomez and a few others have characteristics of retaining memory unlike classical operators.…”
Section: Introductionmentioning
confidence: 99%