Numerical Integral Transform Methods for Random Hyperbolic Models with a Finite Degree of Randomness
| Autor: | M.-C. Casabán, LUCAS JODAR, Rafael Company Rossi |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Random integral transform
General Mathematics Gaussian Mean square random calculus Numerical solution Monte Carlo method Inverse 010103 numerical & computational mathematics 01 natural sciences symbols.namesake Convergence (routing) Computer Science (miscellaneous) Applied mathematics 0101 mathematics Engineering (miscellaneous) Randomness Mathematics lcsh:Mathematics Integral transform lcsh:QA1-939 Random hyperbolic problem 010101 applied mathematics Fourier transform Random Gauss quadrature rules symbols Partial derivative MATEMATICA APLICADA |
| Zdroj: | Mathematics, Vol 7, Iss 9, p 853 (2019) Mathematics Volume 7 Issue 9 RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia Universidad de Alicante (UA) |
| Popis: | [EN] This paper deals with the construction of numerical solutions of random hyperbolic models with a finite degree of randomness that make manageable the computation of its expectation and variance. The approach is based on the combination of the random Fourier transforms, the random Gaussian quadratures and the Monte Carlo method. The recovery of the solution of the original random partial differential problem throughout the inverse integral transform allows its numerical approximation using Gaussian quadratures involving the evaluation of the solution of the random ordinary differential problem at certain concrete values, which are approximated using Monte Carlo method. Numerical experiments illustrating the numerical convergence of the method are included. This work was partially supported by the Ministerio de Ciencia, Innovacion y Universidades Spanish grant MTM2017-89664-P. |
| Databáze: | OpenAIRE |
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