On the series solution of the stochastic Newell Whitehead Segel equation

The purpose of this paper is to present a two-step approach for finding the series solution of the stochastic Newell-Whitehead-Segel (NWS) equation. The proposed two-step approach starts with the use of the Wiener-Hermite expansion (WHE) technique, which allows the conversion of the stochastic problem into a set of coupled deterministic partial differential equations (PDEs) by components. The deterministic kernels of the WHE serve as the solution to the stochastic NWS equation by decomposing the stochastic process. The second step involves solving these PDEs using the reduced differential transform (RDT) algorithm, which enables the determination of the deterministic kernels. The final step involves plugging these kernels back into the WHE to derive the series solution of the stochastic NWS equation. The expectation and variance of the solution are calculated and graphically displayed to provide a clear visual representation of the results. We believe that this two-step technique for computing the series solution process can be used to a great extent for stochastic PDEs arising in a variety of sciences

Solving random diffusion models with nonlinear perturbations by the Wiener-Hermite expansion method

[EN] This paper deals with the construction of approximate series solutions of random nonlinear diffusion equations where nonlinearity is considered by means of a frank small parameter and uncertainty is introduced through white noise in the forcing term. For the simpler but important case in which the diffusion coefficient is time independent, we provide a Gaussian approximation of the solution stochastic process by taking advantage of the Wiener¿Hermite expansion together with the perturbation method. In addition, approximations of the main statistical functions associated with a solution, such as the mean and variance, are computed. Numerical values of these functions are compared with respect to those obtained by applying the Runge¿Kutta second-order stochastic scheme as an illustrative example.This work was partially supported by the Spanish M.C.Y.T. and FEDER grants MTM2009-08587, TRA2007-68006-C02-02, DPI2010-20891-C02-01 as well as the Universidad Politécnica de Valencia grant PAID-06-09 (ref. 2588).Cortés López, JC.; Romero Bauset, JV.; Roselló Ferragud, MD.; Santamaría Navarro, C. (2011). Solving random diffusion models with nonlinear perturbations by the Wiener-Hermite expansion method. Computers and Mathematics with Applications. 61(8):1946-1950. https://doi.org/10.1016/j.camwa.2010.07.057S1946195061

Applying the Wiener-Hermite random technique to study the evolution of excess weight population in the region of Valencia (Spain)

This paper proposes a stochastic model to study the evolution of normal and excess weight population between 24 - 65 years old in the region of Valencia (Spain). An approximate solution process of the random model is obtained by taking advantage of Wiener-Hermite expansion together with a perturbation method (WHEP). The random model takes as starting point a classical deterministic SIS¿type epidemiological model in order to improve it in several ways. Firstly, the stochastic model enhances the deterministic one because it considers uncertainty in its formulation, what it is con- sidered more realistic in dealing with a complex problem as obesity is. Secondly, WHEP approach provides valuable information such as average and variance functions of the approximate solution stochastic process to random model. This fact is remarkable because other techniques only provide predictions in some a priori chosen points. As a conse- quence, we can compute and predict the expectation and the variance of normal and excess weight population in the region of Valencia for any time. This information is of paramount value to both doctors and health authorities to set optimal investment policies and strategies.Cortés López, JC.; Romero Bauset, JV.; Roselló Ferragud, MD.; Villanueva Micó, RJ. (2012). Applying the Wiener-Hermite random technique to study the evolution of excess weight population in the region of Valencia (Spain). American Journal of Computational Mathematics. 2(4):274-281. doi:10.4236/ajcm.2012.24037S2742812

A comparative study to the numerical approximation of random Airy differential equation

The aim of this paper is twofold. First, we deal with the extension to the random framework of the piecewise Fröbenius method to solve Airy differential equations. This extension is based on mean square stochastic calculus. Second, we want to explore the capability to provide not only reliable approximations for both the average and the standard deviation functions associated to the solution stochastic process, but also to save computational time as it happens in dealing with the analogous problem in the deterministic scenario. This includes a comparison of the numerical results with respect to those obtained by other commonly used operational methods such as polynomial chaos and Monte Carlo simulations. To conduct this comparative study, we have chosen the Airy random differential equation because it has highly oscillatory solutions. This feature allows us to emphasize differences between all the considered approaches. © 2011 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish M.C.Y.T. and FEDER grants MTM2009-08587, DPI2010-20891-C02-01 as well as the Universitat Politecnica de Valencia grant PAID-06-09 (Ref. 2588).Cortés López, JC.; Jódar Sánchez, LA.; Romero Bauset, JV.; Roselló Ferragud, MD. (2011). A comparative study to the numerical approximation of random Airy differential equation. Computers and Mathematics with Applications. 62(9):3411-3417. https://doi.org/10.1016/j.camwa.2011.08.056S3411341762

Some recommendations for applying gPC (generalized polynomial chaos) to modeling: An analysis through the Airy random differential equation

In this paper we study the use of the generalized polynomial chaos method to differential equations describing a model that depends on more than one random input. This random input can be in the form of parameters or of initial or boundary conditions. We investigate the effect of the choice of the probability density functions for the inputs on the output stochastic processes. The study is performed on the Airy¿s differential equation. This equation is a good test case since its solutions are highly oscillatory and errors can develop both in the amplitude and the phase. Several different situations are considered and, finally, conclusions are presented.This work has been partially supported by the Spanish M.C.Y.T. and FEDER Grants MTM2009-08587, DPI2010-20891-C02-01 as well as the Universitat Politecnica de Valencia Grants PAID-00-11 (Ref. 2751) and PAID-06-11 (Ref. 2070).Chen Charpentier, BM.; Cortés López, JC.; Romero Bauset, JV.; Roselló Ferragud, MD. (2013). Some recommendations for applying gPC (generalized polynomial chaos) to modeling: An analysis through the Airy random differential equation. Applied Mathematics and Computation. 219(9):4208-4218. https://doi.org/10.1016/j.amc.2012.11.007S42084218219

Solving the random Legendre differential equation: Mean square power series solution and its statistical functions

In this paper we construct, by means of random power series, the solution of second order linear differential equations of Legendre-type containing uncertainty through its coefficients and initial conditions. By assuming appropriate hypotheses on the data, we prove that the constructed random power series solution is mean square convergent. In addition, the main statistical functions of the approximate solution stochastic process generated by truncation of the exact power series solution are given. Finally, we apply the proposed method to some illustrative examples to compare the numerical results for the average and the variance with respect to those obtained by the Monte Carlo approach. © 2011 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish M.C.Y.T. grants MTM2009-08587, DPI2010-20891-C02-01, Universidad Politecnica de Valencia grant PAID06-09-2588 and Mexican Conacyt.Calbo Sanjuán, G.; Cortés López, JC.; Jódar Sánchez, LA.; Villafuerte Altuzar, L. (2011). Solving the random Legendre differential equation: Mean square power series solution and its statistical functions. Computers and Mathematics with Applications. 61(9):2782-2792. https://doi.org/10.1016/j.camwa.2011.03.045S2782279261

Mean Square Analytic Solutions of Random Linear Models

El objetivo de este proyecto de tesis doctoral es el desarrollo de técnicas analítico-numéricas para resolver, en media cuadrática problemas, de valores iniciales de ecuaciones y sistemas de ecuaciones en diferencias y diferenciales aleatorias de tipo lineal. Respecto del estudio aportado sobre ecuaciones en diferencias (véase Capítulo 3), se extienden al contexto aleatorio algunos de los principales resultados que en el caso determinista se conocen para resolver este tipo de ecuaciones así como para estudiar el comportamiento asintótico de su solución. En lo que se refiere a las ecuaciones diferenciales hay que señalar que el elemento unificador del estudio realizado en esta memoria es la extensión al escenario aleatorio del método de Fröbenius para la búsqueda de soluciones de ecuaciones diferenciales en forma de desarrollos en serie de potencias. A largo de los Capítulos 4-7 se abordan problemas tanto de tipo escalar como de tipo matricial tanto de primer como de segundo orden, donde la aleatoriedad se introduce en los modelos a través de las condiciones iniciales y los coeficientes, siendo además la incertidumbre en este último caso, considerada tanto de forma aditiva como multiplicativa. Los problemas basados en ecuaciones diferenciales aleatorias tratados permiten introducir procesos estocásticos importantes como son el proceso exponencial (véase Capítulo 5), los procesos trigonométricos seno y coseno y algunas de sus propiedades algebraicas básicas (véase Capítulo 6). En el último capítulo se estudia la ecuación diferencial de Hermite con coeficientes aleatorios y, bajo ciertas condiciones, se obtienen soluciones en forma de serie aleatoria finita que definen los polinomios de Hermite aleatorios. Además de obtener las soluciones en forma de serie aleatoria convergente en el sentido estocástico de la media cuadrática, para cada uno de los problemas tratados se calculan aproximaciones de las principales propiedades estadísticas del proceso solución.Calbo Sanjuán, G. (2010). Mean Square Analytic Solutions of Random Linear Models [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8721Palanci

COMPUTATIONAL METHODS FOR RANDOM DIFFERENTIAL EQUATIONS: THEORY AND APPLICATIONS

Desde las contribuciones de Isaac Newton, Gottfried Wilhelm Leibniz, Jacob y Johann Bernoulli en el siglo XVII hasta ahora, las ecuaciones en diferencias y las diferenciales han demostrado su capacidad para modelar satisfactoriamente problemas complejos de gran interés en Ingeniería, Física, Epidemiología, etc. Pero, desde un punto de vista práctico, los parámetros o inputs (condiciones iniciales/frontera, término fuente y/o coeficientes), que aparecen en dichos problemas, son fijados a partir de ciertos datos, los cuales pueden contener un error de medida. Además, pueden existir factores externos que afecten al sistema objeto de estudio, de modo que su complejidad haga que no se conozcan de forma cierta los parámetros de la ecuación que modeliza el problema. Todo ello justifica considerar los parámetros de la ecuación en diferencias o de la ecuación diferencial como variables aleatorias o procesos estocásticos, y no como constantes o funciones deterministas, respectivamente. Bajo esta consideración aparecen las ecuaciones en diferencias y las ecuaciones diferenciales aleatorias. Esta tesis hace un recorrido resolviendo, desde un punto de vista probabilístico, distintos tipos de ecuaciones en diferencias y diferenciales aleatorias, aplicando fundamentalmente el método de Transformación de Variables Aleatorias. Esta técnica es una herramienta útil para la obtención de la función de densidad de probabilidad de un vector aleatorio, que es una transformación de otro vector aleatorio cuya función de densidad de probabilidad es conocida. En definitiva, el objetivo de este trabajo es el cálculo de la primera función de densidad de probabilidad del proceso estocástico solución en diversos problemas basados en ecuaciones en diferencias y diferenciales aleatorias. El interés por determinar la primera función de densidad de probabilidad se justifica porque dicha función determinista caracteriza la información probabilística unidimensional, como media, varianza, asimetría, curtosis, etc., de la solución de la ecuación en diferencias o diferencial correspondiente. También permite determinar la probabilidad de que acontezca un determinado suceso de interés que involucre a la solución. Además, en algunos casos, el estudio teórico realizado se completa mostrando su aplicación a problemas de modelización con datos reales, donde se aborda el problema de la estimación de distribuciones estadísticas paramétricas de los inputs en el contexto de las ecuaciones en diferencias y diferenciales aleatorias.Ever since the early contributions by Isaac Newton, Gottfried Wilhelm Leibniz, Jacob and Johann Bernoulli in the XVII century until now, difference and differential equations have uninterruptedly demonstrated their capability to model successfully interesting complex problems in Engineering, Physics, Chemistry, Epidemiology, Economics, etc. But, from a practical standpoint, the application of difference or differential equations requires setting their inputs (coefficients, source term, initial and boundary conditions) using sampled data, thus containing uncertainty stemming from measurement errors. In addition, there are some random external factors which can affect to the system under study. Then, it is more advisable to consider input data as random variables or stochastic processes rather than deterministic constants or functions, respectively. Under this consideration random difference and differential equations appear. This thesis makes a trail by solving, from a probabilistic point of view, different types of random difference and differential equations, applying fundamentally the Random Variable Transformation method. This technique is an useful tool to obtain the probability density function of a random vector that results from mapping another random vector whose probability density function is known. Definitely, the goal of this dissertation is the computation of the first probability density function of the solution stochastic process in different problems, which are based on random difference or differential equations. The interest in determining the first probability density function is justified because this deterministic function characterizes the one-dimensional probabilistic information, as mean, variance, asymmetry, kurtosis, etc. of corresponding solution of a random difference or differential equation. It also allows to determine the probability of a certain event of interest that involves the solution. In addition, in some cases, the theoretical study carried out is completed, showing its application to modelling problems with real data, where the problem of parametric statistics distribution estimation is addressed in the context of random difference and differential equations.Des de les contribucions de Isaac Newton, Gottfried Wilhelm Leibniz, Jacob i Johann Bernoulli al segle XVII fins a l'actualitat, les equacions en diferències i les diferencials han demostrat la seua capacitat per a modelar satisfactòriament problemes complexos de gran interés en Enginyeria, Física, Epidemiologia, etc. Però, des d'un punt de vista pràctic, els paràmetres o inputs (condicions inicials/frontera, terme font i/o coeficients), que apareixen en aquests problemes, són fixats a partir de certes dades, les quals poden contenir errors de mesura. A més, poden existir factors externs que afecten el sistema objecte d'estudi, de manera que, la seua complexitat faça que no es conega de forma certa els inputs de l'equació que modelitza el problema. Tot aço justifica la necessitat de considerar els paràmetres de l'equació en diferències o de la equació diferencial com a variables aleatòries o processos estocàstics, i no com constants o funcions deterministes. Sota aquesta consideració apareixen les equacions en diferències i les equacions diferencials aleatòries. Aquesta tesi fa un recorregut resolent, des d'un punt de vista probabilístic, diferents tipus d'equacions en diferències i diferencials aleatòries, aplicant fonamentalment el mètode de Transformació de Variables Aleatòries. Aquesta tècnica és una eina útil per a l'obtenció de la funció de densitat de probabilitat d'un vector aleatori, que és una transformació d'un altre vector aleatori i la funció de densitat de probabilitat és del qual és coneguda. En definitiva, l'objectiu d'aquesta tesi és el càlcul de la primera funció de densitat de probabilitat del procés estocàstic solució en diversos problemes basats en equacions en diferències i diferencials. L'interés per determinar la primera funció de densitat es justifica perquè aquesta funció determinista caracteritza la informació probabilística unidimensional, com la mitjana, variància, asimetria, curtosis, etc., de la solució de l'equació en diferències o l'equació diferencial aleatòria corresponent. També permet determinar la probabilitat que esdevinga un determinat succés d'interés que involucre la solució. A més, en alguns casos, l'estudi teòric realitzat es completa mostrant la seua aplicació a problemes de modelització amb dades reals, on s'aborda el problema de l'estimació de distribucions estadístiques paramètriques dels inputs en el context de les equacions en diferències i diferencials aleatòries.Navarro Quiles, A. (2018). COMPUTATIONAL METHODS FOR RANDOM DIFFERENTIAL EQUATIONS: THEORY AND APPLICATIONS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/98703TESI

Mathematical Methods, Modelling and Applications

This volume deals with novel high-quality research results of a wide class of mathematical models with applications in engineering, nature, and social sciences. Analytical and numeric, deterministic and uncertain dimensions are treated. Complex and multidisciplinary models are treated, including novel techniques of obtaining observation data and pattern recognition. Among the examples of treated problems, we encounter problems in engineering, social sciences, physics, biology, and health sciences. The novelty arises with respect to the mathematical treatment of the problem. Mathematical models are built, some of them under a deterministic approach, and other ones taking into account the uncertainty of the data, deriving random models. Several resulting mathematical representations of the models are shown as equations and systems of equations of different types: difference equations, ordinary differential equations, partial differential equations, integral equations, and algebraic equations. Across the chapters of the book, a wide class of approaches can be found to solve the displayed mathematical models, from analytical to numeric techniques, such as finite difference schemes, finite volume methods, iteration schemes, and numerical integration methods