Iterative Multistep Reproducing Kernel Hilbert Space Method for Solving Strongly Nonlinear Oscillators
Copyright policy )Iterative Multistep Reproducing Kernel Hilbert Space Method for Solving Strongly Nonlinear Oscillators
A new algorithm called multistep reproducing kernel Hilbert space method is represented to solve nonlinear oscillator’s models. The proposed scheme is a modification of the reproducing kernel Hilbert space method, which will increase the intervals of convergence for the series solution. The numerical results demonstrate the validity and the applicability of the new technique. A very good agreement was found between the results obtained using the presented algorithm and the Runge-Kutta method, which shows that the multistep reproducing kernel Hilbert space method is very efficient and convenient for solving nonlinear oscillator’s models.
- Princess Sumaya University for Technology Jordan
- King Abdulaziz University Saudi Arabia
- University of Jordan Jordan
- Al-Balqa` Applied University Jordan
Artificial intelligence, Support vector machine, Economics, Computational Mechanics, Engineering, Differential equation, Series (stratigraphy), Representer theorem, Physics-Informed Neural Networks for Scientific Computing, Variable Step-Size Algorithms, Physics, Hilbert space, Algorithm, Kernel embedding of distributions, Modeling and Simulation, Physical Sciences, Convergence (economics), Numerical methods for ordinary differential equations, QC1-999, Space (punctuation), Mathematical analysis, Quantum mechanics, Adaptive Filtering in Non-Gaussian Signal Processing, Numerical solutions to equations with linear operators, FOS: Mathematics, Differential algebraic equation, Biology, Anomalous Diffusion Modeling and Analysis, Economic growth, Computational problems in statistics, Pure mathematics, Nonlinear oscillations and coupled oscillators for ordinary differential equations, Paleontology, Statistical and Nonlinear Physics, Applied mathematics, Computer science, Operating system, Kernel method, Physics and Astronomy, Reproducing kernel Hilbert space, Kernel (algebra), Nonlinear system, Mathematics, Nonlinear Systems, Linear multistep method, Ordinary differential equation
Artificial intelligence, Support vector machine, Economics, Computational Mechanics, Engineering, Differential equation, Series (stratigraphy), Representer theorem, Physics-Informed Neural Networks for Scientific Computing, Variable Step-Size Algorithms, Physics, Hilbert space, Algorithm, Kernel embedding of distributions, Modeling and Simulation, Physical Sciences, Convergence (economics), Numerical methods for ordinary differential equations, QC1-999, Space (punctuation), Mathematical analysis, Quantum mechanics, Adaptive Filtering in Non-Gaussian Signal Processing, Numerical solutions to equations with linear operators, FOS: Mathematics, Differential algebraic equation, Biology, Anomalous Diffusion Modeling and Analysis, Economic growth, Computational problems in statistics, Pure mathematics, Nonlinear oscillations and coupled oscillators for ordinary differential equations, Paleontology, Statistical and Nonlinear Physics, Applied mathematics, Computer science, Operating system, Kernel method, Physics and Astronomy, Reproducing kernel Hilbert space, Kernel (algebra), Nonlinear system, Mathematics, Nonlinear Systems, Linear multistep method, Ordinary differential equation
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