KerMor
0.9
Model order reduction for nonlinear dynamical systems and nonlinear approximation
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BellFunctions: Demos regarding the Bell function local Lipschitz estimations. More...
BellFunctions: Demos regarding the Bell function local Lipschitz estimations.
We kindly refer to the article [12] for introduction of bell functions and their theory.
This class is part of the framework
Homepage
http://www.morepas.org/software/index.htmlDocumentation
http://www.morepas.org/software/kermor/index.htmlLicense
KerMor license conditions Definition at line 18 of file BellFunctions.m.
Static Public Member Functions | |
static function PlotManager pm = | SecantGradientPlots (kernels.BellFunction bfun) |
Produces the demo images for maximum secant gradient positions. More... | |
static function | NewtonPenalty (double Gamma) |
Demonstrates the error estimator penalized newton function and maximum secant gradients. More... | |
static function | LocalLipschitzDemo (x0, C) |
ERRORESTDEMO Demo for the monotone radial basis functions error estimator. More... | |
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ERRORESTDEMO Demo for the monotone radial basis functions error estimator.
Definition at line 262 of file BellFunctions.m.
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Demonstrates the error estimator penalized newton function and maximum secant gradients.
Gamma | The \(\gamma\) value to use for the Gaussian bell function. Default: 2 |
Definition at line 129 of file BellFunctions.m.
References k.
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Produces the demo images for maximum secant gradient positions.
For details on the implemented methodology please see [12]
Returns a PlotManager handle if the plots are to be saved.
bfun | A bell function |
pm | A PlotManager instance |
Definition at line 41 of file BellFunctions.m.
References kernels.BellFunction.evaluateD1(), kernels.ARBFKernel.evaluateScalar(), k, PlotManager.LeaveOpen, kernels.BellFunction.ModifiedNewton(), kernels.BellFunction.r0, and kernels.BellFunction.rm.