convdiff_dune_descr.m

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function descr = convdiff_dune_descr(params)
% function model = convdiff_dune_model(params)
% convection diffusion example using dune-rb for high dimensional computations
% and data structures

descr = LinEvol.descr_default;

descr.name            = 'convdiff_dune';
descr.rb_problem_type = 'LinEvolDune';
descr.RB_detailed_data_constructor = @LinEvol.DetailedData;
%descr.RB_reduced_data_constructor  = @LinEvol.ReducedData;
%% data that is definitely used outside of the detailed simulations
%% These values are now set in dunerbconvdiff( 'init_model')

if (~isempty(getenv('DUNERBHOME')))
  addpath( fullfile( getenv('DUNERBHOME'), 'mexclient' ) );
  addpath( getenv('DUNERBHOME') );
end

descr.mexptr = @mexclient;

is_connected = descr.mexptr('is_connected');

if (~is_connected)
  error('No connection to server!');
end

% server_params.serverhost = 'rbevol.uni-muenster.de'
% server_params.port       = '1909'
% mexclient('init_server', server_params)

% copy model data from detailed dune-rb model
tmp_struct = descr.mexptr('init_model');

%model.diffusion = 0;
%model.velocity

descr.T         = tmp_struct.T;
descr.nt        = tmp_struct.nt;
descr.dt        = descr.T / descr.nt;
descr.mu_names  = tmp_struct.mu_names;
descr.mu_ranges = tmp_struct.mu_ranges;
descr.mu        = descr.mexptr('get_mu');


tmp = descr.mexptr('rb_symbolic_coefficients');
sym_coeffs = cell(1,4);
for i = 1:4
  sym_coeffs{i} = '@(mu) [ ';
  for j = 1:size(tmp{i},2);
    if j > 1
      sym_coeffs{i} = [ sym_coeffs{i}, ', ' ];
    end
    sym_coeffs{i} = [ sym_coeffs{i}, tmp{i}{j} ];
  end
  sym_coeffs{i} = [ sym_coeffs{i} , ' ]' ];
end

sym_coeffs = cellfun(@eval, sym_coeffs, 'UniformOutput', false);

descr.coeff_ops = [];
descr.coeff_ops.LL_I_ptr = sym_coeffs{1};
descr.coeff_ops.LL_E_ptr = sym_coeffs{2};
descr.coeff_ops.bb_ptr   = sym_coeffs{3};
descr.coeff_ops.u0_ptr   = sym_coeffs{4};

descr.filecache_ignore_fields_in_model = {'coeff_ops', 'mu'};

descr.verbose = 1;
descr.debug = 0;

descr.data_const_in_time     = 1;

descr.RB_train_num_intervals = 5;

descr.RB_val_size            = 10;

descr.RB_stop_epsilon        = 1e-8;

descr.RB_refinement_mode     = 'adaptive';

%% choose generation mode
%descr.RB_generation_mode = 'greedy_uniform_fixed';
%% alternative:
%% RB_generation_mode = 'random_fixed';

%% choose extension method
%descr.RB_extension_algorithm = @dune_RB_extension_PCA_fixspace;
%% stop timeout
%descr.RB_stop_timeout = 60*60; % 1 hour
%% stop on maximum number of base functions
%descr.RB_stop_Nmax = 30;
%% stop on epsilon
%descr.RB_stop_epsilon = 1e-9; % 0.003 is already realized by init data!!
%% stop on overfitting (needs a validation test set)
%descr.RB_stop_max_val_train_ratio = inf;

%%descr.RB_test_size = 1000;

%% fix the rand seed in case of RB_generation_mode = random_fixed
%descr.RB_train_rand_seed = 0.5;
%% trainig set size in case of RB_generation_mode = random_fixed
%descr.RB_train_size = 50;

%% choose the error indicator
%descr.RB_error_indicator = 'estimator'; % Delta from rb_simulation
%% alternative:
% model.RB_error_indicator = 'error'; % true error

function [mu] = dune_get_mu(model)
  mu = model.mexptr('get_mu');
%  if any(mu(:) ~= model.mu(:))
%    warning(sprintf('mu values in RBmatlab and dune-rb differ! [%s != %s]', mat2str(mu(:)), mat2str(model.mu(:)) ) );
%  end

function reconstruct_and_compare(model, rb_sim_data)
  model.mexptr('reconstruct_and_compare', rb_sim_data.a);
%| \docupdate