Property level critical

Member approx.algorithms.AAdaptiveBase.MaxRelErr

Member approx.algorithms.ABase.ExpConfig

Without this setting this algorithm makes little sense.

Member approx.algorithms.Componentwise.CoeffConfig

Without this setting this algorithm makes little sense.

Member approx.KernelApprox.Algorithm

The approximation strategy for kernel expansions is essential.

Member approx.TPWLApprox.Beta

Member data.selection.EpsSelector.EpsRad

Determines how many training samples are taken. Value MUST be set taking into account the full system's dimension or at least the bounding box of the samples!

Member data.selection.LinspaceSelector.Size

The amount of approximation training data to take.

Member data.selection.TimeSelector.Size

Determines the (maximum) size of the approximation training data samples to take.

Member DPCMDemoClass.Prop1

This is a very critical property!

Member dscomponents.ACoreFun.fDim

Not setting this value in inheriting classes may cause errors in some KerMor algorithms.

Member dscomponents.ACoreFun.TimeDependent

Not setting this value in implementing subclasses causes KerMor's ODE solvers to (possibly) produce wrong results due to wrong assumptions on the time dependence of the core function.

Member dscomponents.ACoreFun.xDim

Not setting this value in inheriting classes may cause errors in some KerMor algorithms.

Member dscomponents.AInputConv.TimeDependent

Not setting this value in implementing subclasses causes KerMor's ODE solvers to (possibly) produce wrong results due to wrong assumptions on the time dependence of the core function.

Member dscomponents.AOutputConv.TimeDependent

Some output conversion matrices are time dependent. This property must be set to the correct value in order for the output conversion to work correctly.

Member general.AffParamMatrix.Matrices

Member general.POD.UseSVDS

Allows for dramatic reduction of computational costs for the price of less accuracy. See the matlab docs for svds and svd.

Member general.regression.BaseScalarSVR.Lambda

Overly regularized functions may not approximate the data correctly, while small \(\lambda\) lead to high coefficient values.

Member general.regression.KernelLS.CGTol

The error tolerance for the pcg method.

Member general.regression.ScalarEpsSVR.Eps

Determines the precision of the approximation.

Member general.regression.ScalarEpsSVR_SMO.Eps

Influences the precision of the result.

Member general.regression.ScalarEpsSVR_SMO.StopEps

Determines the precision of the solution. This eps is used within the stopping criterion introduced in SHS11.

Member general.regression.ScalarNuSVR.nu

Too low \(\nu\) parameters might result in too few support vectors and thus large \(epsilon\). Is closely connected to the C property.

Member kernels.ARBFKernel.Gamma

Greatly influences the kernels behaviour.

Member kernels.BaseKernel.G

If a custom norm is used (i.e. after subspace projection) tihs must be set in order to obtain correct evaluations.

Member kernels.BellFunction.r0

This value is essential for any bell function.

Member kernels.InvMultiquadrics.beta

Greatly influences the kernels behaviour.

Member kernels.InvMultiquadrics.c

Greatly influences the kernels behaviour.

Member kernels.KernelExpansion.Kernel

Correct choice of the system kernel greatly influences the function behaviour.

Member kernels.ParamTimeKernelExpansion.ParamKernel

Correct choice of the system kernel greatly influences the function behaviour.

Member kernels.ParamTimeKernelExpansion.TimeKernel

Correct choice of the time kernel greatly influences the function behaviour.

Member kernels.PolyKernel.Degree

Greatly influences the kernels behaviour.

Member kernels.SigmoidKernel.kappa

Greatly influences the kernels behaviour.

Member kernels.SigmoidKernel.nu

Greatly influences the kernels behaviour.

Member models.BaseFirstOrderSystem.DependentParamIndices

Using more \(\mu\) parameter vector elements in the kernel approximations than actually required in the core function \(f\) introduces an additional dependency of the nonlinearity on extra parameters which is not given in the full model's core function.

Member models.BaseFirstOrderSystem.MaxTimestep

Too large time-steps might violate e.g. CFL conditions and render the solution false and useless.

Member models.BaseFirstOrderSystem.x0

The initial value greatly influences the simulation results.

Member models.BaseFullModel.Approx

The approximation technique is critical for the quality of the reduced model.

Member models.BaseFullModel.SpaceReducer

The subspace computation strategy is critical for the quality of the projection subspace

Member models.BaseModel.dt

Member models.BaseModel.System

No simulations without dynamical system.

Member models.pcd.BasePCDSystem.h

Determines the spatial resolution of the model.

Member models.pcdi.BasePCDISystem.h

Determines the spatial resolution of the model.

Member sampling.ManualSampler.Samples

If manual sampling is used, this property builds the basic set of parameter samples to be used for offline computations and hence must be chosen with maximum care.

Member sampling.RandomSampler.Samples

Determines how many parameter samples are taken and thus directly the offline computation time and model approximation quality.

Member solvers.BaseSolver.MaxStep

Too large steps due to high time-step distances in the passed times vector \(t\) may lead to errorneous results. This property limits the maximum time-step size used in the implementations. Set to [] in order to rely on the times \(t\).

Member solvers.MLode15i.AbsTol

Absolute error tolerances require adoption to the current situation and scale of computations.

Member solvers.MLode15i.RelTol

The correct relative tolerance guarantees the right number of significant figures.

Member solvers.MLWrapper.MLSolver

The correct underlying MatLab builtin solver can make the difference.

Member spacereduction.PODGreedy.Eps

Ultimately determines the projection error on all training trajectories.