Detailed Description

class generating the reduced basis space for the LinEvol problem with a Greedy algorithm.

Parameters given by the constructor argument bg_descr can control the

trainings sample (train_sample_mode, train_num_intervals, train_seed)
trainings sample refinement (refinement_mode, val_size, val_seed, stop_max_val_train_ratio)
the error indicator (indicator_mode)
general greedy controls (Nmax, stop_timeout, stop_epsilon)

Definition at line 18 of file DetailedData.m.

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Public Member Functions
	DetailedData (LinEvol.ReducedModel rmodel,ModelData model_data)
	constructor constructing the reduced basis spaces and storing it in a DataTree. More...

function rb_size =	get_rb_size (IReducedModel rmodel)
	returns the dimension of the stored reduced basis space. More...

Public Member Functions inherited from Greedy.User.IDetailedData
	IDetailedData (BasisGenDescr bg_descr,ModelData model_data)
	constructor forwarding the arguments to IDetailedData constructor More...

function Greedy.DataTree.Detailed.ILeafNode detailed_data_leaf =	get_leaf (Greedy.User.IReducedModel rmodel)
	returns the data tree leaf node for a specific combination of parameters and time indices given by a reduced model. More...

Public Member Functions inherited from IDetailedData
	IDetailedData (BasisGenDescr bg_descr, model_data)
	constructor generating the reduced basis spaces More...

function ret =	subsref (S)
	forwarding of fieldnames access to the underlying model_data struct More...

Public Attributes
Greedy.Interface	bg_algorithm
	a basis generation algorithm used to generated the reduced basis space. More...

	train_sample_mode = "uniform"
	flag indicating whether the parameter sampling for the training set \(M_{\text{train}}\) type. More...

	train_num_intervals = 3
	number of intervals in the ParameterSampling.Uniform for the training set \(M_{\text{train}}\) of the parameter space \({\cal M}\). More...

	train_seed = 1234
	If `train_sample_mode == random`, a ParameterSampling.Random is generated for the training set \(M_{\text{val}}\) with size `train_num_intervals` and seed `train_seed`. More...

	val_size = 100
	If this value is non-zero, a ParameterSampling.Random is generated for the validation set \(M_{\text{val}}\) with size `val_size` and seed `val_seed`. More...

	val_seed = 1234
	If the value of `val_size` is non-zero, a ParameterSampling.Random is generated for the validation set \(M_{\text{val}}\) with size `val_size` and seed `val_seed`. More...

	Nmax = 20
	maximum number of generated reduced basis vectors. More...

	indicator_mode = "estimator"
	error indicator used for the Greedy.Plugin.POD algorithm. More...

	stop_timeout = 60*60
	integer specifying the number of seconds after which the basis generation shall be stopped. More...

	stop_epsilon = 1e-9
	double specifying the maximum error indicator for which the basis generation shall be stopped. More...

	stop_max_val_train_ratio = 1
	positive double value specifying the maximum ratio between the maximum error indicator over the validation paramter set and the maximum error over the trainining parameter set, for which the basis generation is stopped. More...

	refinement_mode = "adaptive"
	string specifying the method type for the adaptation of the parameter sampling. `uniform` or `adaptive` More...

	dune_mode = false

Public Attributes inherited from Greedy.User.IDetailedData
::Greedy.DataTree.Detailed.INode	datatree
	the actual generated data tree

Public Attributes inherited from IDetailedData
BasisGenDescr	bg_descr
	struct describing how the basis shall be generated. More...

::ModelData	model_data
	struct holding \(H\)-dimensional model data, which is needed for an IDetailedModel.detailed_simulation(), e.g. a grid object.

Constructor & Destructor Documentation

LinEvol.DetailedData.DetailedData	(	LinEvol.ReducedModel	rmodel,
		ModelData	model_data
	)

constructor constructing the reduced basis spaces and storing it in a DataTree.

Parameters

rmodel	object specifying how the reduced simulations can be computed.
model_data	Matlab structure storing (possibly) high dimensional data needed by IDetailedModel.detailed_simulation().

Required fields of rmodel:

bg_descr — bg descr
detailed_model — detailed model

Definition at line 187 of file DetailedData.m.

Member Function Documentation

function rb_size = LinEvol.DetailedData.get_rb_size ( IReducedModel rmodel )

virtual

returns the dimension of the stored reduced basis space.

Parameters

rmodel model specifying which basis space dimension shall be returned in case different spaces have been generated different parameters, time intervals or variables. In most implementations this parameter is unused.

Return values

rb_size rb size

Implements IDetailedData.

Definition at line 282 of file DetailedData.m.

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Member Data Documentation

LinEvol.DetailedData.bg_algorithm

a basis generation algorithm used to generated the reduced basis space.

generates a default basis generation object for linear evolution problems as given by a DetailedModel.

Note: This property has the MATLAB attribute Dependent set to true.; This property has non-standard access specifiers: SetAccess = Private, GetAccess = Public; Matlab documentation of property attributes.

This function generates a Greedy.Interface implementation, with at least a POD-Greedy extension algorithm for the reduced basis.

Generated fields of basis_gen:

refinement_mode — refinement mode

Note: [readonly]

Definition at line 38 of file DetailedData.m.

LinEvol.DetailedData.indicator_mode = "estimator"

error indicator used for the Greedy.Plugin.POD algorithm.

string specifying which indicators shall be used by the error_indicators() method.

Possible values are: error for an error between the detailed and the reduced computation. estimator for an a posteriori error estimator

Note: This property is an abstract property without implementation.; Matlab documentation of property attributes.

Default: "error"

Default: "estimator"

Definition at line 124 of file DetailedData.m.

LinEvol.DetailedData.Nmax = 20

maximum number of generated reduced basis vectors.

Default: 20

Definition at line 115 of file DetailedData.m.

LinEvol.DetailedData.refinement_mode = "adaptive"

string specifying the method type for the adaptation of the parameter sampling. uniform or adaptive

Possible values are

uniform: the parameter sampling is refined uniformly
adaptive: the parameter sampling is refined adaptively, such that only regions of the parameter space with high estimated errors are refined.
none: no Greedy.TrainingSetAdaptation object is created

Default: "adaptive"

Definition at line 166 of file DetailedData.m.

LinEvol.DetailedData.stop_epsilon = 1e-9

double specifying the maximum error indicator for which the basis generation shall be stopped.

Default: 1e-9

Definition at line 144 of file DetailedData.m.

LinEvol.DetailedData.stop_max_val_train_ratio = 1

positive double value specifying the maximum ratio between the maximum error indicator over the validation paramter set and the maximum error over the trainining parameter set, for which the basis generation is stopped.

If this value is set to something less than inf, parameter sampling adaptation should be activated, by setting the refinement_mode to adaptive or uniform.

Default: 1

Definition at line 153 of file DetailedData.m.

LinEvol.DetailedData.stop_timeout = 60*60

integer specifying the number of seconds after which the basis generation shall be stopped.

Default: 60*60

Definition at line 135 of file DetailedData.m.

LinEvol.DetailedData.train_num_intervals = 3

number of intervals in the ParameterSampling.Uniform for the training set \(M_{\text{train}}\) of the parameter space \({\cal M}\).

Default: 3

Definition at line 73 of file DetailedData.m.

LinEvol.DetailedData.train_sample_mode = "uniform"

flag indicating whether the parameter sampling for the training set \(M_{\text{train}}\) type.

Possible values

uniform: (default) for a ParameterSampling.Uniform
random: for a ParameterSampling.Random

Default: "uniform"

Definition at line 59 of file DetailedData.m.

LinEvol.DetailedData.train_seed = 1234

If train_sample_mode == random, a ParameterSampling.Random is generated for the training set \(M_{\text{val}}\) with size train_num_intervals and seed train_seed.

Default: 1234

Definition at line 83 of file DetailedData.m.

LinEvol.DetailedData.val_seed = 1234

If the value of val_size is non-zero, a ParameterSampling.Random is generated for the validation set \(M_{\text{val}}\) with size val_size and seed val_seed.

Default: 1234

Definition at line 104 of file DetailedData.m.

LinEvol.DetailedData.val_size = 100

If this value is non-zero, a ParameterSampling.Random is generated for the validation set \(M_{\text{val}}\) with size val_size and seed val_seed.

Default: 100

Definition at line 94 of file DetailedData.m.

The documentation for this class was generated from the following file:

rbasis/problem_types/+LinEvol/DetailedData.m

Detailed Description

Public Member Functions

Public Attributes

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation