Reactivate Runge Kutta Convolution Quadrature #128
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- Make latest RKCQ version suitable for Julia 1.9 - Only neccesary changes - No further improvements and adjustments
Second derivative of Gaussian added to enable the usage of the derivative of the Gaussian as a right hand side. This has the advantage that the right hand side does not contain DC information. Added inverse Fourier transform to solve time-domain problem by inverse Fourier transforming the frequency-domain problem.
RKCQ uses now the ConvolutionOperators interface that is used by Space-Time-Galerkin MOT. To this end, all information required for RKCQ is contained in the StagedTimeStep type, which is used for building the SpaceTimeBasis. Moreover, the bilinear form can now be build with retarded potentials.
We compare against Space-Time-Galerkin MOT Alternatively one could compare the individual frequencies against EFIE frequency solutions. However, this results in a considerably longer run time.
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Unit test fail at motlu.jl:9, could you please take a look? |
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Looks like a missing eltype implementation. Could be ConvolutionOperators.jl is to blame. If you confirm this, I will patch and register... |
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I think the eltype has been added recently to the ConvolutionOperators.jl. Maybe there needs to be a version bump somewhere? Probably, we used a dev version of ConvolutionOperators.jl |
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RKCQ uses now the ConvolutionOperators interface that is used by
Space-Time-Galerkin MOT.
To this end, all information required for RKCQ is contained in the
StagedTimeStep type, which is used for building the SpaceTimeBasis.
Moreover, the bilinear form can now be build with retarded potentials.
We also add a unit test where we compare against Space-Time-Galerkin MOT
Alternatively one could compare the individual frequencies against
analytical EFIE frequency solutions, obtained from the SphericalScattering package.
However, this results in a considerably longer run time.
To build a quasi analytic solution by inverse Fourier transforming Mie solutions,
we add the inverse Fourier transform, as well as the 2nd derivative of the Gaussian
to BEAST's special functions. The solution is not part of BEAST but we think that
these two functions can be also helpful for other developers.