48 this =
this@error.alpha.Base(model);
54 if ~isempty(fm.System.B)
60 warning(
" Some:Id ",
" Error estimator for current system will not work correctly! (B is not linear and mu-independent! ");
64 B2 = B - rm.
V*(rm.
W^
t*B);
65 this.M2= M^
t*(rm.
G*B2);
66 this.M3= B2^
t*(rm.
G*B2);
90 a = phi*this.
M1*phi^
t;
93 a = a + phi*this.M2*ut + ut^t*this.M3*ut;
function a = getAlpha(colvec phi,double ut,double t,colvec mu)
Computes the alpha term for the error estimator.
Constant: Constant alpha terms.
The base class for any KerMor detailed model.
models.BaseFullModel FullModel
The full model this reduced model was created from.
models.BaseFirstOrderSystem System
The actual dynamical system used in the model.
dscomponents.AInputConv B
The input conversion.
matrix< double > G
The custom scalar product matrix .
The KerMor reduced model class.
matrix< double > V
The matrix that has been used for projection.
matrix< double > W
The biorthogonal matrix for V, i.e. .
Constant(models.BaseFullModel model)
function matrix< double > mu = getRandomParam(integer num,integer seed)
Gets a random parameter sample from the system's parameter domain P.
function inputOfflineComputations(models.ReducedModel rm,matrix< double > M)
Performs the offline stage for the error estimators regarding the inputs.