Simple Polynomial Solutions for Some Nonlinear Differential Equations
Keywords:
Nonlinear differential equations, polynomial solutions, symbolic computingAbstract
This study examines the possibilities of simple polynomial solutions for specific classes of nonlinear differential equations. It relies on the traditional algebraic approach to solving nonlinear polynomial systems, where projective methods and summations play an important role. The study focuses on first- and second-order nonlinear ordinary differential equations, using the Ansatz method and symbolic computing tools to generate and validate polynomial solutions of order four or less. The main objectives include discovering equations with polynomial solutions, evaluating the effectiveness of analytical methods such as equilibrium analysis and Lie symmetry, and comparing the validity of exact solutions with approximate and numerical alternatives. The research adopts an analytical-theoretical approach, relying on algebraic logic, symbolic software (Mathematica/Maple), and a carefully selected sample of well-known nonlinear models, including the Riccati and Duffing equations. The results demonstrate that, under certain structural and boundary conditions, nonlinear equations can allow for simple polynomial solutions that accurately describe the behavior of the system. The results also demonstrate that the form and degree of nonlinearity of the equation significantly influence the feasibility of developing polynomial solutions. The study emphasizes the importance of symbolic computing in verifying these solutions and suggests further research on higher-order systems with variable coefficients, as well as the integration of polynomial solutions into applicable physical and engineering environments.
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1. Canny, J. F., Kaltofen, E., & Yagati, L. (1989, July). Solving systems of nonlinear polynomial equations faster. In Proceedings of the ACM-SIGSAM 1989 international symposium on Symbolic and algebraic computation (pp. 121-128).
2. Stamenković, M. (2012). Nonlinear Differential Equations in Current Research of System Nonlinear Dynamics. Scientific Technical Review, 62(3-4), 62-69.
3. Zarnan, J. A., Hameed, W. M., & Kanbar, A. B. (2022). New numerical approach for solution of nonlinear differential equations. Journal of Hunan University Natural Sciences, 49(7).
4. Folland, G. B. (2020). Introduction to partial differential equations (Vol. 102). Princeton university press.
5. Miller, R. K., & Michel, A. N. (2014). Ordinary differential equations. Academic press.
6. Lisle, I. (1992). Equivalence transformations for classes of differential equations (Doctoral dissertation, University of British Columbia).
7. Ibragimov, N. K. (1992). Group analysis of ordinary differential equations and the invariance principle in mathematical physics (for the 150th anniversary of Sophus Lie). Russian Mathematical Surveys, 47(4), 89.
8. Jordan, D., & Smith, P. (2007). Nonlinear ordinary differential equations: an introduction for scientists and engineers (No. 10). Oxford University Press.
9. Söderlind, G. (1984). On nonlinear difference and differential equations. BIT Numerical Mathematics, 24(4), 667-680.
10. Azad, H., Laradji, A., & Mustafa, M. T. (2011). Polynomial solutions of differential equations. Advances in Difference Equations, 2011(1), 58.
11. Sherbrooke, E. C., & Patrikalakis, N. M. (1993). Computation of the solutions of nonlinear polynomial systems. Computer Aided Geometric Design, 10(5), 379-405.
12. Dickenstein, A. (2005). Solving polynomial equations. Springer. Simple Polynomial Solutions.
13. Yun, D. Y. (1973). On algorithms for solving systems of polynomial equations. ACM SIGSAM Bulletin, (27), 19-25.
14. The Ansatz method for constructing polynomial solutions Mukhin, E., & Varchenko, A. (2006). Quasi-polynomials and the Bethe ansatz. arXiv preprint math/0604048.
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