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Postprocess.StochasticAssessment.Assessment Class Reference

Detailed Description

a class used to compute reduced several reduced simulations over a huge parameter sample extracting useful information

The class generates one or two dimensional cell arrays of these data fields storing information for a variation of some attributes of the reduced model, which can be freely chosen by specification of plot_fields .

Definition at line 19 of file Assessment.m.

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Public Member Functions

 Assessment (matfile,IReducedModel rmodel, mu_set_size, seed)
 constructor More...
 
function output = compute ()
 stochastic estimation of error between reduced and detailed simulation over a test sample of \(\mu\) vectors More...
 
function  init_random_sampling (mu_set_size, seed)
 generates the initial random sample set M_test More...
 
function other = copy ()
 deep copy of this object More...
 
function  equal_distribution_samples (min, max, sample_size)
 adapts the samples attribute such that they are equally distributed in a given range. More...
 
function  update_rmodel (plot_field, new_value)
 updates the reduced model, i.e. applies the changes defined by the plot_fields attribute More...
 

Static Public Member Functions

static function [

rb_sim_data , tictoc ] = 
rb_simulation_tictoc (varargin)
 a wrapper around the IReducedModel.rb_simulation() measuring the execution time More...
 

Public Attributes

 plot_fields = {"[]"}
 is a vector of field names with at most 2 elements over which the error landscape is computed or a cell with empty entries. More...
 
 samples = {"(0.1:0.1:1)"}
 is a cell array of scalar row vectors. The scalar values are plot_field values for which the error is computed and tested. More...
 
ParameterSampling.Interface M_test
 an parameter sampling object More...
 
 plot_field_descr = {""}
 is a cell array of description texts for the plot fields. If this field is not set it is set to plot_fields. More...
 
 run_name
 character string specifying this test run. The string should be unique for every parameter combination. More...
 
 compute_estimates = false
 boolean value determining whether error estimates shall be computed More...
 
 compute_errors = false
 boolean value determining whether the "true" error \(\|u_h(\mu) - u_{\text{red}}(\mu)\|_{{\cal W}_h}\) shall be computed More...
 
 compute_conditions = false
 boolean value determining whether the condition numbers of the system matrices shall be computed More...
 
 follow_refinement_steps = false
 boolean value indicating whether the refinement steps of the reduced basis generation by Greedy.TrainingSetAdaptation shall be taken into account More...
 
 M_by_N_ratio = 1
 if M and N are couple this value specifies the constant for the coupling. More...
 
::IReducedModel rmodel
 the underlying reduced model
 
::CacheableObject cache_object
 an object storing the reduced model and the detailed data
 
 rsamples
 the "real" collection of vectors used for the plot_fields variation More...
 
Greedy.DataTree.Detailed.INode detailed_data
 the underlying detailed data More...
 

Constructor & Destructor Documentation

Postprocess.StochasticAssessment.Assessment.Assessment (   matfile,
IReducedModel  rmodel,
  mu_set_size,
  seed 
)

constructor

Parameters
matfilename of result file where a IReducedModel object and a Greedy.DataTree.Detailed.INode object must be stored.
rmodelobject specifying how the reduced simulations can be computed.
mu_set_sizeOptional argument determining the number of random parameters in the validation sample. (Default = 10)
seeda random seed used for initialization of ParameterSampling.Random object for the M_test parameter sample set. (Default = 654321)
Required fields of rmodel:
  • descr.name —  descr.name

Definition at line 226 of file Assessment.m.

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Member Function Documentation

function output = Postprocess.StochasticAssessment.Assessment.compute ( )

stochastic estimation of error between reduced and detailed simulation over a test sample of \(\mu\) vectors

This function stochastically estimates the error between reduced and detailed simulations for given \(\mu\)-vectors and various reduced and collateral basis sizes. The results are visualized in a surface plot for problems with CRB.

If required by the users, averaged time measurements for the reduced and the detailed simulations are computed, too.

Return values
outputan Output object
Generated fields of output:
  • rd_conds —  rd conds

Definition at line 279 of file Assessment.m.

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function other = Postprocess.StochasticAssessment.Assessment.copy ( )

deep copy of this object

Return values
othercopied Assessment object

Definition at line 441 of file Assessment.m.

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function Postprocess.StochasticAssessment.Assessment.equal_distribution_samples (   min,
  max,
  sample_size 
)

adapts the samples attribute such that they are equally distributed in a given range.

Parameters
minis a vector of minimum values of plot_fields variables.
maxis a vector for maximum values of plot_fields variables.
sample_sizespecifies the number of sample values between min and max for which the error is computed and plotted. The default value is max-min.

Definition at line 452 of file Assessment.m.

function Postprocess.StochasticAssessment.Assessment.init_random_sampling (   mu_set_size,
  seed 
)

generates the initial random sample set M_test

Parameters
mu_set_sizenumber of parameters in the sample
Default: 100
seeda random seed for initialization of the random generator (Default = random value initialized with clock)

Definition at line 423 of file Assessment.m.

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function [ rb_sim_data , tictoc ] = Postprocess.StochasticAssessment.Assessment.rb_simulation_tictoc (   varargin)
static

a wrapper around the IReducedModel.rb_simulation() measuring the execution time

Parameters
vararginvarargin
Return values
rb_sim_datathe reduced simulation data
tictocexecution time

Definition at line 512 of file Assessment.m.

function Postprocess.StochasticAssessment.Assessment.update_rmodel (   plot_field,
  new_value 
)

updates the reduced model, i.e. applies the changes defined by the plot_fields attribute

Parameters
plot_fieldan entry in plot_fields, if it is empty, a coupling of N and M is assured.
new_valuethe new value it should be set to.

Definition at line 483 of file Assessment.m.

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Member Data Documentation

Postprocess.StochasticAssessment.Assessment.compute_conditions = false

boolean value determining whether the condition numbers of the system matrices shall be computed


Default: false

Definition at line 117 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.compute_errors = false

boolean value determining whether the "true" error \(\|u_h(\mu) - u_{\text{red}}(\mu)\|_{{\cal W}_h}\) shall be computed


Default: false

Definition at line 107 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.compute_estimates = false

boolean value determining whether error estimates shall be computed


Default: false

Definition at line 98 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.detailed_data

the underlying detailed data

Todo:
make this cached...
Note
This property has the MATLAB attribute Dependent set to true.
Matlab documentation of property attributes.
[readonly]

Definition at line 188 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.follow_refinement_steps = false

boolean value indicating whether the refinement steps of the reduced basis generation by Greedy.TrainingSetAdaptation shall be taken into account

This option is only useful if

  1. M_test is set to the training sample of the parameter space generation and
  2. the plot_fields include the basis dimensions In this case the parameter sampling used during basis generation for the given basis dimension is selected.


Default: false

Definition at line 127 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.M_by_N_ratio = 1

if M and N are couple this value specifies the constant for the coupling.

A value of zero means \(M_{\max} / N_{\max}\)


Default: 1

Definition at line 144 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.M_test

an parameter sampling object

Default: is a random sampling with 10 elements and seed 654321;

See also
init_random_sampling()

Definition at line 64 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.plot_field_descr = {""}

is a cell array of description texts for the plot fields. If this field is not set it is set to plot_fields.


Default: {""}

Definition at line 76 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.plot_fields = {"[]"}

is a vector of field names with at most 2 elements over which the error landscape is computed or a cell with empty entries.

A reasonable choice would be '{ 'N', 'M' }'. The error landscape is plotted over a variation of these fields specified by samples. In case of an empty cell entry, N and M are coupled by the fixed ratio M_by_N_ratio and samples should be a vector of coupling constant between 0 and 1.

See also
update_rmodel()

Default: = {[]}

Definition at line 36 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.rsamples

the "real" collection of vectors used for the plot_fields variation

See also
samples
Note
This property has the MATLAB attribute Dependent set to true.
Matlab documentation of property attributes.
[readonly]

Definition at line 175 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.run_name

character string specifying this test run. The string should be unique for every parameter combination.

Default: rmodel.name + _stoachastic_assessment;

Definition at line 87 of file Assessment.m.

Postprocess.StochasticAssessment.Assessment.samples = {"(0.1:0.1:1)"}

is a cell array of scalar row vectors. The scalar values are plot_field values for which the error is computed and tested.

Default: = {[0.1:0.1:1]}

Definition at line 53 of file Assessment.m.


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