Solucion numérico-analítica de una ecuación de difusión bajo condiciones de incertidumbre utilizando DTM y Monte Carlo

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Gilberto González Parra http://orcid.org/0000-0001-5847-678X
Abraham J Arenas http://orcid.org/0000-0003-3106-1271
Miladys Cogollo http://orcid.org/0000-0002-6583-4693

Keywords

modelos lineales de difusión aleatorios, condiciones de incertidumbre, Esquemas de diferencias finitas, método de transformación diferencial, solución analítica-numérica

Resumen

Un método numérico para resolver una ecuación parabólica general aleatoria lineal donde el coeficiente de difusión, el término fuente, las condiciones de contorno e iniciales incluyen la incertidumbre, se ha desarrollado. Ecuaciones de difusión surgen en muchos campos de la ciencia y la ingeniería, y en muchos casos, existen la incertidumbres debido a los datos que no se pueden saber, o debido a errores en las mediciones y la variabilidad intrínseca. Para modelar estas incertidumbres los parámetros correspondientes, coeficiente de difusión, término fuente, condiciones de contorno e iniciales, se suponen que son variables aleatorias con determinadas distribuciones de probabilidad. Basándose en los resultados numéricos, se obtienen los intervalos de confianza y valores medios esperados para la solución. Además, se obtienen con las soluciones numéricas-analíticas del método híbrido propuesto.

MSC: 35K10, 65C05, 65M99, 35R60

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Referencias

[1] F. Boano, R. Revelli, and L. Ridolfi, “Stochastic modelling of DO and BOD components in a stream with random inputs,” Advances in Water Resources, vol. 29, no. 9, pp. 1341 – 1350, 2006.

[2] B. Chen-Charpentier, B. Jensen, and P. Colberg, “Random Coefficient Differential Models of Growth of Anaerobic Photosynthetic Bacteria,” ETNA, vol. 34, pp. 44–58, 2009.

[3] B. Oksendal, Stochastic Differential Equations. Springer, New York, 1995.

[4] T. Soong, Probabilistic Modeling and Analysis in Science and Engineering. Wiley, New York, 1992.

[5] B. M. Chen-Charpentier, J.-C. Cortés, J.-V. Romero, and M.-D. Roselló, “Some recommendations for applying gPC (generalized polynomial chaos) to modeling: An analysis through the Airy random differential equation,” Applied Mathematics and Computation, vol. 219, no. 9, pp. 4208 – 4218, 2013.

[6] G. González-Parra, B. Chen-Charpentier, and A. J. Arenas, “Polynomialchaos for random fractional order differential equations,” Applied Mathematics and Computation, vol. 226, pp. 123 – 130, 2014.

[7] E. Vanden Eijnden, “Studying random differential equations as a tool for turbulent diffusion,” Phys. Rev. E, vol. 58, no. 5, pp. R5229–R5232, Nov 1998. [Online]. Available: http://link.aps.org/doi/10.1103/PhysRevE.58.R5229

[8] B. Kegan and R. West, “Modeling the simple epidemic with deterministic differential equations and random initial conditions,” Math. Biosc, vol. 195, pp. 197–193, 2005.

[9] H. Kim, Y. Kim, and D. Yoon, “Dependence of polynomial chaos on random types of forces of KdV equations,” Applied Mathematical Modelling, vol. 36, no. 7, pp. 3080 – 3093, 2012.

[10] J. Wu, Y. Zhang, L. Chen, and Z. Luo, “A Chebyshev interval method for nonlinear dynamic systems under uncertainty,” Applied Mathematical Modelling, vol. 37, no. 6, pp. 4578 – 4591, 2013.

[11] S. Bhatnagar and Karmeshu, “Monte-Carlo estimation of time-dependent statistical characteristics of random dynamical systems,” Applied Mathematical Modelling, vol. 35, no. 6, pp. 3063 – 3079, 2011.

[12] G. Gonzalez-Parra, L. Acedo, and A. Arenas, “Accuracy of analyticalnumerical solutions of the Michaelis-Menten equation,” Computational & Applied Mathematics, vol. 30, no. 2, pp. 445–461, 2011.

[13] L. Villafuerte and B. Chen-Charpentier, “A random differential transform method: Theory and applications,” Applied Mathematics Letters, vol. 25, no. 10, pp. 1490–1494, 2012.

[14] F. Morrison, The Art of Modeling Dynamic Systems. John Wiley, 1991. 52 [15] Y. Chen, J. Liu, and G. Meng, “Incremental harmonic balance method for nonlinear flutter of an airfoil with uncertain-but-bounded parameters,” Applied Mathematical Modelling, vol. 36, no. 2, pp. 657 – 667, 2012.

[16] V. Mallet and B. Sportisse, “Air quality modeling: From deterministic to stochastic approaches,” Comput. Math. Appl, vol. 55, no. 10, pp. 2329–2337, 5 2008.

[17] S. D. Brown, R. Ratcliff, and P. L. Smith, “Evaluating methods for approximating stochastic differential equations,” Journal of Mathematical Psychology, vol. 50, no. 4, pp. 402–410, 8 2006.

[18] S. Wu, “The Euler scheme for random impulsive differential equations,” Applied Mathematics and Computation, vol. 191, no. 1, pp. 164–175, 2007.

[19] S. R. Hanna, J. C. Chang, and M. E. Fernau, “Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (uam-iv) due to uncertainties in input variables,” Atmospheric Environment, vol. 32, no. 21, pp. 3619–3628, 1998.

[20] K. M. Hanson, “A framework for assessing uncertainties in simulation predictions,” Physica D: Nonlinear Phenomena, vol. 133, no. 1-4, pp. 179–188, 1999.

[21] G. Pukhov, Differential Transformations of Functions and Equations. Naukova Dumka (in Russian), 1980.

[22] J. Zhou, Differential Transformation and its Applications for Electrical Circuits. Huazhong University Press, Wuhan (in Chinese), 1986.

[23] J. Biazar and M. Eslami, “Differential transform method for quadratic Riccati differential equation,” International Journal of Nonlinear Science, vol. 9, no. 4, pp. 444–447, 2010.

[24] A. J. Arenas, G. González-Parra, and B. M. Chen-Charpentier, “Dynamical analysis of the transmission of seasonal diseases using the differential transformation method,” Mathematical and Computer Modelling, vol. 50, no. 5–6, pp. 765 – 776, 2009. [Online]. Available: http://dx.doi.org/10.1016/j.mcm.2009.05.005

[25] I. A.-H. Hassan, “Application to differential transformation method for solving systems of differential equations,” Applied Mathematical Modelling, vol. 32, pp. 2552–2559, 2008.

[26] M. Jang and C. Chen, “Analysis of the response of a strongly nonlinear damped system using a differential transformation technique,” Applied Mathematics and Computation, vol. 88, pp. 137–151, 1997.

[27] V. S. Ertürk, G. Zaman, and S. Momani, “A numeric–analytic method for approximating a giving up smoking model containing fractional derivatives,” Computers & Mathematics with Applications, vol. 64, no. 10, pp. 3065–3074,2012.

[28] C. Bervillier, “Status of the differential transformation method,” Applied Mathematics and Computation, vol. 218, no. 20, pp. 10 158 – 10 170, 2012.

[29] I. Hwang, J. Li, and D. Du, “A numerical algorithm for optimal control of a class of hybrid systems: differential transformation based approach,” International Journal of Control, vol. 81, no. 2, pp. 277–293, 2008. [Online]. Available: http://dx.doi.org/10.1080/00207170701556880

[30] C. E. Mejía and D. A. Murio, “Numerical identification of diffusivity coef-ficient and initial condition by discrete mollification,” Comput. Math. Appl, vol. 12, pp. 35–50, 1995.

[31] C.-K. Chen and S.-P. Ju, “Application of differential transformation to transient advective-dispersive transport equation,” Applied Mathematics and Computation, vol. 155, no. 1, pp. 25 – 38, 2004.