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, max_refinement_level, refinement_theta, val_size, val_seed, stop_max_val_train_ratio)
general EI extension controls (noof_ei_extensions) and
general greedy controls (stop_Nmax, stop_Mmax, stop_timeout, stop_epsilon, stop_max_val_train_ratio)

Definition at line 18 of file DetailedData.m.

Inheritance diagram for TwoPhaseFlow.DetailedData:

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Collaboration diagram for TwoPhaseFlow.DetailedData:

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

function datatree =	gen_single_deim_detailed_data (IReducedModel rmodel,ModelData model_data, checkpoint)

function datatree =	gen_single_detailed_data (IReducedModel rmodel,ModelData model_data, checkpoint)

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 respectively number of vectors in the ParameterSampling.Random for the training set \(M_{\text{train}}\) of the parameter space \({\cal M}\). More...

	train_seed = 4321
	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...

	refinement_mode = "adaptive"
	sets the Greedy.TrainingSetAdaptation.refinement_mode option More...

	max_refinement_level = 5
	sets the Greedy.TrainingSetAdaptation.max_refinement_level option More...

	refinement_theta = 0.2
	sets the Greedy.TrainingSetAdaptation.refinement_theta option More...

	val_size = 10
	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...

	stop_Mmax = 200
	sets the Greedy.Plugin.EI.stop_Mmax option More...

	stop_Nmax = 50
	sets the Greedy.Plugin.PODEI.stop_Nmax option More...

	stop_epsilon = 1e-5
	sets the Greedy.Algorithm.stop_epsilon option More...

	stop_max_val_train_ratio = 1.2
	sets the Greedy.Algorithm.stop_max_val_train_ratio option More...

	stop_timeout = 126060
	sets the Greedy.Algorithm.stop_timeout option More...

	noof_ei_extensions = 1
	sets the number of ei extensions More...

	pca = 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

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

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().
checkpoint	checkpoint

Required fields of rmodel:

bg_descr — bg descr
detailed_model — detailed model

Definition at line 220 of file DetailedData.m.

Member Function Documentation

function rb_size = TwoPhaseFlow.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 387 of file DetailedData.m.

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Member Data Documentation

TwoPhaseFlow.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 either a subsequent generation of empirical interpolation bases spaces for all operators and a reduced basis space afterwards, or a combined generation of all reduced spaces with the Greedy.Plugin.PODEI variant of the Greedy.Algorithm.

Note: [readonly]

Definition at line 39 of file DetailedData.m.

TwoPhaseFlow.DetailedData.max_refinement_level = 5

sets the Greedy.TrainingSetAdaptation.max_refinement_level option

If this level is reached, the algorithm terminates and returns the generated reduced basis spaces.

Default: 5

Definition at line 116 of file DetailedData.m.

TwoPhaseFlow.DetailedData.noof_ei_extensions = 1

sets the number of ei extensions

See Greedy.Plugin.EI.noof_ei_extensions for details.

Default: 1

Definition at line 204 of file DetailedData.m.

TwoPhaseFlow.DetailedData.refinement_mode = "adaptive"

sets the Greedy.TrainingSetAdaptation.refinement_mode option

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 100 of file DetailedData.m.

TwoPhaseFlow.DetailedData.refinement_theta = 0.2

sets the Greedy.TrainingSetAdaptation.refinement_theta option

This value \(\theta\) is multiplied by the number of leaf elements in the current parameter grid in order to compute the number grid cells to be refined.

Default: 0.5

Default: 0.2

Definition at line 127 of file DetailedData.m.

TwoPhaseFlow.DetailedData.stop_epsilon = 1e-5

sets the Greedy.Algorithm.stop_epsilon option

Default: 1e-5

Definition at line 177 of file DetailedData.m.

TwoPhaseFlow.DetailedData.stop_max_val_train_ratio = 1.2

sets the Greedy.Algorithm.stop_max_val_train_ratio option

Default: 1.2

Definition at line 186 of file DetailedData.m.

TwoPhaseFlow.DetailedData.stop_Mmax = 200

sets the Greedy.Plugin.EI.stop_Mmax option

Default: 200

Definition at line 159 of file DetailedData.m.

TwoPhaseFlow.DetailedData.stop_Nmax = 50

sets the Greedy.Plugin.PODEI.stop_Nmax option

Default: 50

Definition at line 168 of file DetailedData.m.

TwoPhaseFlow.DetailedData.stop_timeout = 12*60*60

sets the Greedy.Algorithm.stop_timeout option

Default: 12*60*60

Definition at line 195 of file DetailedData.m.

TwoPhaseFlow.DetailedData.train_num_intervals = 3

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

Default: 3

Definition at line 78 of file DetailedData.m.

TwoPhaseFlow.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
single: constructs a basis from a single trajectory of solution snapshots (cog of parameter sampling set).

Default: "uniform"

Definition at line 62 of file DetailedData.m.

TwoPhaseFlow.DetailedData.train_seed = 4321

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: 4321

Definition at line 89 of file DetailedData.m.

TwoPhaseFlow.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 148 of file DetailedData.m.

TwoPhaseFlow.DetailedData.val_size = 10

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: 10

Definition at line 138 of file DetailedData.m.

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

rbasis/problem_types/+TwoPhaseFlow/DetailedData.m

Detailed Description

Public Member Functions

Public Attributes

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation