inverse_target index data_item_name data_item_index data_item_number

This record, in combination with the inverse_target_data record with the same index, allows you to specify target data (e.g. experimental data to determine model parameters or required data for optimization) to be used in the inverse modeling. Parameters will be determined by least squares minimization, i.e. the sum

$$\Sigma { ( penalty \; ( actual\_value - target\_value ) ) } ^ 2$$

is minimized ($penalty$ and $target\_value$ are the penalty factor and target value, see inverse_target_data). In the example below, the temperature (2.33) at node 3 and the temperature (80.2) at post point 10 were measured and are used for inverse modeling of the conductivity. Both experimental results are imposed with a penalty factor 100 in the inverse modeling. In total 20 iterations are used to estimate the conductivity. The initial estimate for the conductivity needs to be specified via the group_condif_conductivity record; while iterating this record will be updated with new estimates.

...
condif_temperature
end_initia
...
inverse_target 1 -node_dof 3 -temp
inverse_target_data 1 2.33 100.
inverse_target 2 -post_point_dof 10 -temp
inverse_target_data 2 80.2 100.
inverse_parameter 0 -group_condif_conductivity 0 0
inverse_iterations 0 20
...
end_data