Results 161 to 170 of about 10,988,171 (189)

Parameter selection by discrete mollification and the numerical solution of the inverse heat conduction problem [PDF]

Journal of Computational and Applied Mathematics, 1988
A procedure for the numerical solution of the one-dimensional inverse heat conduction problem, based on the computaion of the solution associated with a suitable filtered version of the noisy data by discrete mollification is presented and a parameter ...
Diego A Murio
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A mollification method for a Cauchy problem for the Helmholtz equation

International Journal of Computer Mathematics, 2017
ABSTRACTThe Cauchy problem for the Helmholtz equation is considered. This problem is severely ill-posed, that is, the solution does not depend continuously on the data.
Zhenping Li, C. Xu, M. Lan, Z. Qian
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A mollification regularization method for stable analytic continuation

Mathematics and Computers in Simulation, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhi-Liang Deng, Chu-Li Fu, Xiao-Li Feng, Yuan-Xiang Zhang   +3 more
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A Jacobi spectral method for calculating fractional derivative based on mollification regularization

Asymptotic Analysis, 2023
In this article, we construct a Jacobi spectral collocation scheme to approximate the Caputo fractional derivative based on Jacobi–Gauss quadrature. The convergence analysis is provided in anisotropic Jacobi-weighted Sobolev spaces. Furthermore, the convergence rate is presented for solving Caputo fractional derivative with noisy data by invoking the ...
Zhang, Wen, Wu, Changxing, Ruan, Zhousheng, Qiu, Shufang   +3 more
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Some Applications of the Mollification Method

2001
The Mollification Method is a filtering procedure that is appropriate for the regularization of a variety of ill-posed problems. In this review, we briefly introduce the method, including its main feature, which is its ability to automatically select regularization parameters.
C. E. Mejía, D. A. Murio, S. Zhan
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Numerical identification of diffusivity coefficient and initial condition by discrete mollification [PDF]

Computers and Mathematics With Applications, 1995
We discuss the simultaneous identification of the initial condition and the space-time depending diffusivity coefficient for general linear one-dimensional parabolic equations when the measured information is obtained only at the active boundary.We solve
D A Murio
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A New Mollification Method for Numerical Differentiation of 2D Periodic Functions

2009 International Joint Conference on Computational Sciences and Optimization, 2009
In this paper, we present a new method for numerical differentiation of bivariate periodic functions when a set of noisy data is given. TSVD is chosen as the needed regularization technique. It turns out the new method coincides with some type of truncated Fourier series approach. A numerical example is also given to show the efficiency of the method.
Zhenyu Zhao, Zehong Meng, Li Xu, Junfeng Liu   +3 more
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Automatic numerical solution of generalized 2-D IHCP by discrete mollification [PDF]

Computers and Mathematics With Applications, 2001
A space marching scheme, based on the mollification method and generalized cross validation, is described to numerically recover the temperature and heat flux histories in the two-dimensional generalized inverse heat conduction problem.
D A Murio
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A mollification regularization method for unknown source in time-fractional diffusion equation

International Journal of Computer Mathematics, 2014
In the present paper, we consider an inverse source problem for a fractional diffusion equation. This problem is ill-posed, i.e. the solution (if it exists) does not depend continuously on the data. We give the mollification regularization method to solve this problem.
Fan Yang 0026, Chu-Li Fu, Xiao-Xiao Li
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Determining an Unknown Source in the Heat Equation by a Mollification Regularization Method

2010 International Conference on Computational Intelligence and Software Engineering, 2010
The problem of identifying an unknown source in the heat equation is ill-posed in the sense that the solution(if it exists) does not depend continuously on the data. In this paper, we proposed a regularization strategy-mollification method to analysis the stability of the problem. Meanwhile, we proposed numerical implement.
Ailin Qian, Yongxin Gui
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