rb_reconstruction_dictionary.m

function rb_sim_data = rb_reconstruction_dictionary(model, RB_dict,rb_sim_data)%dict_extracted_reduced_data , rb_sim_data)
%function rb_sim_data = rb_reconstruction_dictionary(model_data, detailed_data, rb_sim_data)
%
% function computing a detailed reconstruction by linear
% combination of the coefficients in the simulation data with the
% reduced basis vector extracted from the dicitonary.
% The sensitivity derivative solution is also reconstructed. Either using
% the common reduced basis or separte derivative information reduced bases.
%
% Required fields of RB_dict:
%   Phi:    The dictionary reduced basis
%
% Optional fields of RB_dict:
%   Phi_der:    The basis for derivative information in the dictionary
%
% Required fileds in dict_extracted_reduced_data:
%   extraction_vector:  Vector containing the indices of elements extracted
%                       for rb_simulation.
%
% Required fields of rb_sim_data:
%   a:  The coefficient vector from rb_simulation.
%

% Markus Dihlmann 30.10.2011

RB = [RB_dict.init_base,RB_dict.Phi];
RB = RB(:,rb_sim_data.extraction_vector);
rb_sim_data.U = RB * rb_sim_data.a;

if model.compute_derivative_info
    if ~isfield(RB_dict,'Phi_der')
        for i = 1:size(rb_sim_data.c,3)
            rb_sim_data.U_der(:,:,i) = RB* rb_sim_data.c(:,:,i);
        end
    else
        indx_mu=find(model.optimization.params_to_optimize);
        for i = 1:sum(model.optimization.params_to_optimize)
            RB_der = [RB_dict.init_base, RB_dict.Phi_der{indx_mu(i)}];
            rb_sim_data.U_der(:,:,i) = RB_der(:,dict_extracted_reduced_data.extraction_vector) * rb_sim_data.c{i}(:,:);
        end
    end
end
%calculate output
model.decomp_mode=0;
v = model.operators_output(model,RB_dict);
  rb_sim_data.output = (v(:)') * rb_sim_data.U;
  if model.compute_derivative_info
  for i=1:size(rb_sim_data.U_der,3)
    rb_sim_data.output_derivative(:,i) = (v(:)') * rb_sim_data.U_der(:,:,i);
  end
  end