Results 171 to 180 of about 1,268,193 (209)
On an inverse problem of approximation theory in the Bloch space
Anton Baranov +2 more
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Orthogonal Constrained Neural Networks for Solving Structured Inverse Eigenvalue Problems
Shuai Zhang +3 more
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ANTI-ABEL INTEGRAL EQUATION AND INVERSE PROBLEMS (Mathematical Models in Functional Equations)
, 2000 Yutaka Kamimura
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Journal of the Optical Society of America A, 2000
We apply functional analysis to the scattered electromagnetic field of a particle with spherical symmetry to obtain a pair of integral transforms for converting the Mie-scattering amplitudes S perpendicular (theta) and S parallel (theta) into the Mie coefficients an and bn.
I K, Ludlow, J, Everitt
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We apply functional analysis to the scattered electromagnetic field of a particle with spherical symmetry to obtain a pair of integral transforms for converting the Mie-scattering amplitudes S perpendicular (theta) and S parallel (theta) into the Mie coefficients an and bn.
I K, Ludlow, J, Everitt
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Foundations of Science, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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SIAM Review, 1998
In the inverse eigenvalue problem, one has to construct a matrix with a (partially) given spectrum. The problem appears in many different forms and in many different applications. Usually the problem is constrained in the sense that the matrix \(M\) that one wants to find has to be in a certain class. For example it should be of the form \(M=A+X\) or \(
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In the inverse eigenvalue problem, one has to construct a matrix with a (partially) given spectrum. The problem appears in many different forms and in many different applications. Usually the problem is constrained in the sense that the matrix \(M\) that one wants to find has to be in a certain class. For example it should be of the form \(M=A+X\) or \(
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1998
Click on the DOI link to access the article (may not be free). ; In this chapter, we consider the second-order parabolic equation (9.0.1) a0∂tu − div(a∇u) + b · ∇u + cu = f in Q = Ω × (0, T), where Ω is a bounded domain the space Rn with the C2-smooth boundary ∂Ω.
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Click on the DOI link to access the article (may not be free). ; In this chapter, we consider the second-order parabolic equation (9.0.1) a0∂tu − div(a∇u) + b · ∇u + cu = f in Q = Ω × (0, T), where Ω is a bounded domain the space Rn with the C2-smooth boundary ∂Ω.
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Journal of Microscopy, 1989
Positron emission tomography involves constructing an image of brain tissue from gamma rays counted at detectors surrounding the head. This is an inverse problem: how to measure a phenomenon from data taken from a derived distribution. We have noise and a loss of high frequency signal, both of which contribute to ill‐conditioning.
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Positron emission tomography involves constructing an image of brain tissue from gamma rays counted at detectors surrounding the head. This is an inverse problem: how to measure a phenomenon from data taken from a derived distribution. We have noise and a loss of high frequency signal, both of which contribute to ill‐conditioning.
openaire +1 more source