Maier Verfurth 2021a - Revision history

Scipediacontent at 14:54, 26 June 2021

2021-06-26T14:54:35Z

Scipediacontent: Scipediacontent moved page Draft Content 904224483 to Maier Verfurth 2021a

2021-06-26T14:52:12Z

Scipediacontent moved page Draft Content 904224483 to Maier Verfurth 2021a

Scipediacontent: Created page with "== Abstract == A multiscale approach for a nonlinear Helmholtz problem with possible oscillations in the Kerr coefficient, the refractive index, and the diffusion coefficient..."

2021-06-26T14:52:10Z

Created page with "== Abstract == A multiscale approach for a nonlinear Helmholtz problem with possible oscillations in the Kerr coefficient, the refractive index, and the diffusion coefficient..."

New page

== Abstract ==

A multiscale approach for a nonlinear Helmholtz problem with possible oscillations in the Kerr coefficient, the refractive index, and the diffusion coefficient is presented. The method does not rely on structural assumptions on the coefficients and combines the multiscale technique known as Localized Orthogonal Decomposition with an adaptive iterative approximation of the nonlinearity. The method is rigorously analyzed in terms of well-posedness and convergence properties based on suitable assumptions on the initial data and the discretization parameters. Numerical examples illustrate the theoretical error estimates and underline the practicability of the approach.

== Video ==
{{#evt:service=cloudfront|id=258786|alignment=center|filename=79.mp4}}

== Document ==
<pdf>Media:Draft_Content_904224483A_IDC6_79.pdf</pdf>

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-== Abstract ==
+== Summary ==
 A multiscale approach for a nonlinear Helmholtz problem with possible oscillations in the Kerr coefficient, the refractive index, and the diffusion coefficient is presented. The method does not rely on structural assumptions on the coefficients and combines the multiscale technique known as Localized Orthogonal Decomposition with an adaptive iterative approximation of the nonlinearity. The method is rigorously analyzed in terms of well-posedness and convergence properties based on suitable assumptions on the initial data and the discretization parameters. Numerical examples illustrate the theoretical error estimates and underline the practicability of the approach.
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 == Video ==
 {{#evt:service=cloudfront|id=258786|alignment=center|filename=79.mp4}}

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