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Continuous operator method application for direct and inverse scattering problems

open access: yesЖурнал Средневолжского математического общества, 2021
We describe the continuous operator method for solution nonlinear operator equations and discuss its application for investigating direct and inverse scattering problems.
Boykov Ilya V.   +3 more
doaj   +5 more sources

ON APPLYING THE CONTINUOUS OPERATOR METHOD TO SOLVE THE DIRECT PROBLEM FOR NONLINEAR PARABOLIC EQUATIONS

open access: yesИзвестия высших учебных заведений. Поволжский регион: Физико-математические науки, 2020
Background. Parabolic differential equations of mathematical physics play very important role in mathematical modeling of the wide range of phenomena in physical and technical sciences.
I. V. Boykov, V. A. Ryazantsev
doaj   +2 more sources

APPLICATION OF THE CONTINUOUS OPERATOR METHOD TO THE SOLUTION OF THE POCKLINGTON AND GALLEN EQUATIONS FOR THIN WIRE ANTENNAS

open access: yesИзвестия высших учебных заведений. Поволжский регион: Физико-математические науки, 2020
Background. One of the central tasks in microwave electronics is the construction of miniature antennas with high performance. The main equations used in modeling wire antennas of various configurations are the Pocklington, Gallen equations, singular ...
I. V. Boykov, P. V. Aykashev
doaj   +2 more sources

Continuous modified Newton’s-type method for nonlinear operator equations [PDF]

open access: yesAnnali di Matematica Pura ed Applicata, 2003
.A nonlinear operator equation F(x)=0, F:H→H, in a Hilbert space is considered. Continuous Newton’s-type procedures based on a construction of a dynamical system with the trajectory starting at some initial point x0 and becoming asymptotically close to a
A. Ramm, A. Smirnova, A. Favini
semanticscholar   +5 more sources

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels [PDF]

open access: yes2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes.
Fey, Matthias   +3 more
core   +2 more sources

The Unitary Correlation Operator Method from a Similarity Renormalization Group Perspective [PDF]

open access: yes, 2007
We investigate how the Unitary Correlation Operator Method (UCOM), developed to explicitly describe the strong short-range central and tensor correlations present in the nuclear many-body system, relates to the Similarity Renormalization Group (SRG), a ...
Hergert, H., Roth, R.
core   +4 more sources

Improved Regularization Method for Backward Cauchy Problems Associated with Continuous Spectrum Operator [PDF]

open access: yesInternational Journal of Differential Equations, 2011
We consider in this paper an abstract parabolic backward Cauchy problem associated with an unbounded linear operator in a Hilbert space 𝐻, where the coefficient operator in the equation is an unbounded self-adjoint positive operator which has a ...
Salah Djezzar, Nihed Teniou
doaj   +4 more sources

PMLSM position control based on continuous projection adaptive sliding mode controller

open access: yesSystems Science & Control Engineering, 2018
In this paper, the design of projection-based Adaptive Sliding Mode Controller (ASMC) is presented for position control of Permanent Magnet Linear Synchronous Motor (PMLSM) with unknown mover mass.
Amjad Jaleel Humaidi   +1 more
doaj   +2 more sources

A short note on an adaptive damped Newton method for strongly monotone and Lipschitz continuous operator equations [PDF]

open access: yesArchiv der Mathematik, 2022
We consider the damped Newton method for strongly monotone and Lipschitz continuous operator equations in a variational setting. We provide a very accessible justification why the undamped Newton method performs better than its damped counterparts in a ...
Pascal Heid
semanticscholar   +1 more source

Geometry-Informed Neural Operator for Large-Scale 3D PDEs [PDF]

open access: yesNeural Information Processing Systems, 2023
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying geometries.
Zong-Yi Li   +10 more
semanticscholar   +1 more source

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