dom_dec_model.m

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function model = dom_dec_model(base_model,params)
% function model = dom_dec_model(base_model,params)
% function creating a domain-decomposition-model with an arbitrary
% model and optional params. 
%
% The domain in model is decomposed into
% two parts. one can specify multiple rectangles in
% params.dd_rect_corner1/2, which are used for constructing the
% grids of the first subdomain. the second subdomain then is the
% complement of the first.

% I.Maier, 01.07.2011

% default parameters
if nargin < 2
  params = [];
end;

if ~isfield(params,{'dd_rect_corner1','dd_rect_corner2'})
  params.dd_rect_corner1 = {[0,0],[0.25,0],[0.75,0]};
  params.dd_rect_corner2 = {[0.25,0.25],[0.75,0.75],[1,0.5]};
end;
if ~isfield(params,'RB_extension_method')
  params.RB_extension_method = 'A';
end;
if ~isfield(params,'RB_sequence_mode')
  params.RB_sequence_mode = 0;
end;
if ~isfield(params,'RB_numintervals')
  params.RB_numintervals = 4;
end;

model = [];
model.verbose = 1;
model.decomp_mode = 0; %default
model.base_model = base_model;
model.dirichlet_side = 1; % 1 or 2

if isfield(base_model,'mu_names')
  model.mu_names = base_model.mu_names;
  model.mu_ranges = base_model.mu_ranges;

  model.mu_ref = zeros(length(model.mu_ranges),1);
  for i = 1:length(model.mu_ranges)
    model.mu_ref(i) = (model.mu_ranges{i}(1) + ...
                       model.mu_ranges{i}(2))/2;
  end;
end;

% required funtion pointers for the simulations
model.set_mu = @dom_dec_set_mu;
model.get_mu = @get_mu_default;
model.gen_model_data = @dom_dec_gen_model_data;
model.operators = @dom_dec_operators;
model.operators_output = base_model.operators_output;
model.detailed_simulation = @dom_dec_detailed_simulation;
model.get_dofs_from_sim_data = @dom_dec_get_dofs_from_sim_data;
model.gen_detailed_data = @dom_dec_gen_detailed_data;
model.gen_reduced_data = @dom_dec_gen_reduced_data;
model.get_rb_size = @dom_dec_get_rb_size;
model.orthonormalize = @dom_dec_orthonormalize;
model.rb_simulation = @dom_dec_rb_simulation;
model.rb_reconstruction = @dom_dec_rb_reconstruction;
model.compute_error = @dom_dec_compute_error;
model.plot_sim_data = @dom_dec_plot_sim_data;
model.get_inner_product_matrices = @ ...
    dom_dec_get_inner_product_matrices;
model.set_rb_in_detailed_data = @dom_dec_set_rb_in_detailed_data;
model.get_rb_from_detailed_data = @ ...
    dom_dec_get_rb_from_detailed_data;
model.RB_extension_PCA_fixspace = @ ...
    dom_dec_RB_extension_PCA_fixspace;
model.RB_extension_eigenbasis = @ dom_dec_RB_extension_eigenbasis;

% geometry:
model.dd_rect_corner1 = params.dd_rect_corner1;
model.dd_rect_corner2 = params.dd_rect_corner2;

% settings for the detailed simulation:
model.maxiter = 10000;
model.det_sim_tol = 1e-12;
model.relaxation_factor = 1; % the optimal relaxation parameter is
                             % multiplied by this factor

% settings for the basis generation:
model.RB_generation_mode = 'POD_greedy';
model.RB_generation_epsilon = 1e-14;
model.RB_extension_method = params.RB_extension_method;
model.RB_extension_PCA_modes = 1;
model.RB_sequence_mode = params.RB_sequence_mode;
model.N_max = {100 100};

model.RB_init_mode = 'random';
model.N_eigenbasis = 0;
model.RB_error_indicator = 'rb_error_estimate';
model.RB_greedy_tolerance = 1e-06;

if isfield(base_model,'mu_names')
  model.RB_numintervals = params.RB_numintervals * ...
      ones(length(base_model.mu_names),1);
end;

% settings for the reduced simulation:
model.RB_approximate_thetaN = 1;
model.RB_maxiterN = 10000;
model.RB_sim_tol = 1e-12;

if isfield(base_model,'coercivity_alpha')
  model.coercivity_alpha = base_model.coercivity_alpha;
end;
if isfield(base_model,'continuity_gamma')
  model.continuity_gamma = base_model.continuity_gamma;
end;

if isfield(base_model,'mu_names')
  % default mu values
  model = set_mu_default(model,get_mu(base_model));
end;