greedy basis generation extension which adaptively refines the trainings parameter set.
Definition at line 18 of file TrainingSetAdaptation.m.
Public Member Functions | |
TrainingSetAdaptation (Greedy.Interface child) | |
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@(child.rb_engine, child.model); Gree dy.In terf ace | |
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 | |
Greedy.TrainingSetAdaptation.TrainingSetAdaptation | ( | Greedy.Interface | child | ) |
constructor extending a Greedy algorithm object
child | the greedy algorithm to be extended by training set adaptation. |
Definition at line 144 of file TrainingSetAdaptation.m.
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.
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 |
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.
rmodel | object specifying how the reduced simulations can be computed. |
model_data | Matlab structure storing (possibly) high dimensional data needed by IDetailedModel.detailed_simulation(). |
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
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. |
eta | eta |
Definition at line 276 of file TrainingSetAdaptation.m.
Greedy.TrainingSetAdaptation.element_indicator_mode = "nodes_cogs" |
string specifying the indicator for the training set nodes.
nodes_cogs
nodes
nodes_skippedrefs
nodes_cogs_skippedrefs
Default: "nodes_cogs"
Definition at line 104 of file TrainingSetAdaptation.m.
Greedy.TrainingSetAdaptation.element_indicator_s_max = 100 |
specifies maximum number of skipped refinements in case of *_skippedrefs
indicator modes.
Default: 100
Definition at line 119 of file TrainingSetAdaptation.m.
Greedy.TrainingSetAdaptation.force_stop_at_max_refinement_level = true |
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 |
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.
Greedy.TrainingSetAdaptation.max_refinement_level = 5 |
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.
Greedy.TrainingSetAdaptation.max_refinement_steps = inf |
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.
Greedy.TrainingSetAdaptation.refinement_mode = "uniform" |
string specifying the method type for the adaptation of the parameter sampling. uniform
or adaptive
uniform
: the parameter sampling is refined uniformlyadaptive
: 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.
Greedy.TrainingSetAdaptation.refinement_theta = 0.5 |
ratio of elements which are refined during an adaptive refinement step
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
Definition at line 91 of file TrainingSetAdaptation.m.