mettlesome
发表于 2025-3-25 03:25:33
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Entreaty
发表于 2025-3-25 08:27:21
Comparing Numerical Methods for Inverse Source Problem in Time-Fractional Diffusion Equation,rse source problem as an nonsmooth optimization problem. Two different alternative methods are applied to find the solution of the control problem. The accuracy of the proposed methods is tested in the cases of with and without noise, and the findings are very promising and encouraging.
finale
发表于 2025-3-25 13:58:32
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radiograph
发表于 2025-3-25 16:26:44
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certitude
发表于 2025-3-25 23:12:43
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你不公正
发表于 2025-3-26 03:07:16
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防锈
发表于 2025-3-26 05:55:41
Meshless Methods to Noninvasively Calculate Neurocortical Potentials from Potentials Measured at thce. However, accurate estimation of neurocortical potentials from scalp potentials is a challenging problem, due to the complex and ill-posed nature of the forward and inverse problems. In this paper, we present an approach based on the annular model and polynomial expansion method to solve the Cauc
2否定
发表于 2025-3-26 08:54:51
Solving Geometric Inverse Problems with a Polynomial Based Meshless Method,connected bounded domain ., given the Dirichlet temperature data ., the Neumann heat flux data . on the boundary ., and a Dirichlet boundary condition on the boundary .. We assume that the temperature . satisfies the modified Helmholtz equation governing the conduction of heat in a fin. The proposed
警告
发表于 2025-3-26 15:14:05
Image Restoration Using a Coupled Reaction-Diffusion Equations,sing the image decomposition strategy of . norm given by Guo et al. [.]. This choice is motivated by the success of the decomposition strategy in preserving and illustrating the small features in the textured image. Our results include a rigorous discretization of the proposed model, and numerical r
Ordeal
发表于 2025-3-26 20:22:55
A Novel Identification Scheme of an Inverse Source Problem Based on Hilbert Reproducing Kernels,imal solution is established. To solve the obtained optimization problem an approach based on the reproducing kernel Hilbert spaces is proposed. The proposed optimization method based on the gradient descent algorithm is examined through some numerical experiments. The effectiveness of the proposed