Results 291 to 300 of about 13,648,456 (359)
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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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Large steps in inverse rendering of geometry
ACM Transactions on Graphics, 2021Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to ...
Baptiste Nicolet +2 more
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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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IMA Journal of Numerical Analysis, 2020
Physics-informed neural networks (PINNs) have recently been very successfully applied for efficiently approximating inverse problems for partial differential equations (PDEs).
Siddhartha Mishra, R. Molinaro
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Physics-informed neural networks (PINNs) have recently been very successfully applied for efficiently approximating inverse problems for partial differential equations (PDEs).
Siddhartha Mishra, R. Molinaro
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Deep-Learning Schemes for Full-Wave Nonlinear Inverse Scattering Problems
IEEE Transactions on Geoscience and Remote Sensing, 2019This paper is devoted to solving a full-wave inverse scattering problem (ISP), which is aimed at retrieving permittivities of dielectric scatterers from the knowledge of measured scattering data.
Zhun Wei, Xudong Chen
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Adaptive Fuzzy Inverse Optimal Control for Uncertain Strict-Feedback Nonlinear Systems
IEEE transactions on fuzzy systems, 2020This article first investigates the adaptive fuzzy inverse optimal control design problem for a class of uncertain strict-feedback nonlinear systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent ...
Yong-ming Li, Xiao Min, Shaocheng Tong
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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.
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Inverse problem theory - and methods for model parameter estimation
, 2004A. Tarantola
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