rbmatlab  1.13.10

## Detailed Description

greedy basis generation extension which adaptively refines the trainings parameter set.

Todo:

Definition at line 18 of file TrainingSetAdaptation.m.

Inheritance diagram for Greedy.TrainingSetAdaptation:
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Collaboration diagram for Greedy.TrainingSetAdaptation:
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## Public Member Functions

constructor extending a Greedy algorithm object

function IDetailedData
detailed_data =
init_basis (Greedy.User.IReducedModel rmodel,ModelData model_data)
construction of an initial reduced basis

function Greedy.User.IDetailedData
detailed_data =
basis_extension (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data, checkpoint)
basis extension routine

function eta = refinement_element_indicators (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data,Greedy.DataTree.Detailed.ILeafNode dd_leaf)
evaluates the refinement indicators

Public Member Functions inherited from Greedy.MetaInterface
MetaInterface (child)
mbgi = mbgi@.nosp@m.Gree.nosp@m.dy.In.nosp@m.terf.nosp@m.ace(child.rb_engine, child.model);

function  prepare (IReducedModel rmodel,ModelData model_data)
initialization routine for basis extension

Public Member Functions inherited from Greedy.Interface
function Greedy.Combined summ = horzcat ()
combines an arbitrary number of basis generation algorithms

function
detailed_data =
gen_detailed_data (IReducedModel rmodel, detailed_data,Greedy.Checkpoint checkpoint)
main entry function construction the detailed data tree storing the reduced basis functions.

virtual function Greedy.DataTree.Detailed.INode
detailed_data =
init_basis (IReducedModel rmodel,ModelData model_data)
construction of an initial reduced basis

virtual function Greedy.DataTree.Detailed.INode
detailed_data =
basis_extension (IReducedModel rmodel,Greedy.DataTree.Detailed.INode detailed_data, checkpoint)
basis extension routine

function value = get_generated_basis_type ()
returns the generated_basis_type property. Why???

## Public Attributes

max_refinement_steps = inf
maximum number of refinement steps before the algorithm stops.

max_refinement_level = 5
maximum number of refinement levels in the refineable trainings parameter set

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

refinement_theta = 0.5
ratio of elements which are refined during an adaptive refinement step

element_indicator_mode = "nodes_cogs"
string specifying the indicator for the training set nodes.

element_indicator_s_max = 100
specifies maximum number of skipped refinements in case of *_skippedrefs indicator modes.

force_stop_at_max_refinement_level = true
boolean flag indicating whether at the maximum refinement level the algorithm shall stop.

Public Attributes inherited from Greedy.MetaInterface
child

id

generated_basis_type

Public Attributes inherited from Greedy.Interface
generated_basis_type
string specifying the detailed data produced by this basis generation algorithm object.

id
This is an id string inherited by the underlying extension algorithm object implementing a Greedy.Plugin.Interface.

boolean enable_checkpointing = true
control variable variable controlling whether check points shall be created after every reduced basis extension step.

## Static Public Attributes

static const  info_fields
cell array of field names to be copied to the detailed data node instance during init_basis() call

## Constructor & Destructor Documentation

constructor extending a Greedy algorithm object

Parameters
 child the greedy algorithm to be extended by training set adaptation.

Definition at line 144 of file TrainingSetAdaptation.m.

## Member Function Documentation

 function Greedy.User.IDetailedData detailed_data = Greedy.TrainingSetAdaptation.basis_extension ( Greedy.User.IReducedModel rmodel, Greedy.User.IDetailedData detailed_data, checkpoint )

basis extension routine

basis extension routine This method is run by the gen_detailed_data() method at the very end, and shall actually construct the reduced basis.

Parameter values
checkpoint: an object of type Greedy.Checkpoint used for checkpointing.
Parameters
 rmodel object specifying how the reduced simulations can be computed. detailed_data object defining the basis generation algorithm and storage for storing high dimensional data, i.e. dependent on dimension $$H$$. This data is necessary for detailed simulations, construction of online matrices, reduced_data and reconstruction of reduced simulations. checkpoint checkpoint
Return values
 detailed_data object storing the reduced basis information in the leaf nodes and information on the reduced basis generation in every node.

copied from basisgen_refined written by

Definition at line 174 of file TrainingSetAdaptation.m.

 function IDetailedData detailed_data = Greedy.TrainingSetAdaptation.init_basis ( Greedy.User.IReducedModel rmodel, ModelData model_data )

construction of an initial reduced basis

construction of an initial reduced basis This method is called at the beginning of the gen_detailed_data() method right after a call to prepare(). It should be used to construct an initial reduced basis, e.g. approximating the initial value functions in case of evolution problems.

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().
Return values
 detailed_data object storing the reduced basis information in the leaf nodes and information on the reduced basis generation in every node.

Definition at line 157 of file TrainingSetAdaptation.m.

 function eta = Greedy.TrainingSetAdaptation.refinement_element_indicators ( Greedy.User.IReducedModel rmodel, Greedy.User.IDetailedData detailed_data, Greedy.DataTree.Detailed.ILeafNode dd_leaf )

evaluates the refinement indicators

Parameters
 rmodel object specifying how the reduced simulations can be computed. detailed_data object defining the basis generation algorithm and storage for storing high dimensional data, i.e. dependent on dimension $$H$$. This data is necessary for detailed simulations, construction of online matrices, reduced_data and reconstruction of reduced simulations. dd_leaf Detailed data node which is currently extended leaf in the detailed data tree.
Return values
 eta eta

Definition at line 276 of file TrainingSetAdaptation.m.

## Member Data Documentation

string specifying the indicator for the training set nodes.

Possible values
• nodes_cogs
• nodes
• nodes_skippedrefs
• nodes_cogs_skippedrefs

Default: "nodes_cogs"

Definition at line 104 of file TrainingSetAdaptation.m.

specifies maximum number of skipped refinements in case of *_skippedrefs indicator modes.

Default: 100

Definition at line 119 of file TrainingSetAdaptation.m.

boolean flag indicating whether at the maximum refinement level the algorithm shall stop.

If this is set to false, no further refinement is done at the last level, but the reduced basis spaces are generated, anyways.

Default: true

Definition at line 129 of file TrainingSetAdaptation.m.

static
Initial value:
= {" 'max_refinement_steps', 'max_refinement_level', \
'refinement_mode', 'refinement_theta', 'element_indicator_mode', \
'element_indicator_s_max', 'force_stop_at_max_refinement_level' "}

cell array of field names to be copied to the detailed data node instance during init_basis() call

Default: {" 'max_refinement_steps', 'max_refinement_level', \ 'refinement_mode', 'refinement_theta', 'element_indicator_mode', \ 'element_indicator_s_max', 'force_stop_at_max_refinement_level' "}

Definition at line 33 of file TrainingSetAdaptation.m.

maximum number of refinement levels in the refineable trainings parameter set

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

Default: 5

Definition at line 62 of file TrainingSetAdaptation.m.

maximum number of refinement steps before the algorithm stops.

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

Default: inf

Definition at line 50 of file TrainingSetAdaptation.m.

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.

Default: "uniform"

Definition at line 75 of file TrainingSetAdaptation.m.

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.