# model_discrepancy

(Experimental) Post-calibration calculation of model discrepancy correction

**Specification**

*Alias:*None*Arguments:*None

**Child Keywords:**

Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|

Optional |
Specify the type of model discrepancy |
||

Optional |
Specify number of prediction locations for model discrepancy |
||

Optional |
List prediction locations for model discrepancy |
||

Optional |
Specify text file containing prediction configurations for model discrepancy |
||

Optional |
Output file for prediction discrepancy calculations |
||

Optional |
Output file for corrected model prediction calculations |
||

Optional |
Output file for prediction variance calculations |

**Description**

The goal of parameter calibration is to minimize the difference between experimental data, \(d(x)\) , and model observations, \(M(\theta, x)\) , where \(\theta\) are the model parameters and \(x\) is a configuration variable, such as temperature or pressure. However, it is not uncommon that, at the conclusion of parameter calibration, the agreement between experimental data and the calibrated model is not “close enough.” This is often due to model form or structural error. In this case, the canonical equation

is replaced by one that also includes model discrepancy \(\delta(x)\) ,

In the Dakota implementation, the calculation of \(\delta(x)\) is performed after the model parameters \(\theta\) are calibrated. For each observable \(d_i\) , the discrepancy

is calculated for each value \(x_j\) of the configuration variable, where
\(\theta^*\) is the optimal parameter value obtained during the
calibration. For scalar responses, the model discrepancy is only a function of
the configuration variables, and there is one discrepancy regression model for
each observable \(d_{i}\) .
This set of discrepancy
models may be specified to be either Gaussian process or polynomial regression
models of constant, linear, or quadratic order, and each model is fit to the
calculated discrepancy values. See the
`discrepancy_type`

command for more details regarding these options.
For field responses, the model discrepancy is
a function of the configuration variables as well as the independent field
coordinates (such as time or space), and there is one discrepancy regression
model for each field.
In this case, the discrepancy models are Gaussian process models. The
calculation of model discrepancy has not been tested for cases in which
responses are mixed scalar and field responses.

The user
may then specify new or “prediction” configurations at which the corrected
model \(M(\theta^*, x_{new}) + \delta(x_{new})\) should be calculated, using
one of the `num_prediction_configs`

, `prediction_configs`

, or
`import_prediction_configs`

keywords. If none of these keywords is specified,
the number of prediction configurations is set to 10 for scalar responses.
The corresponding prediction variances are also calculated, according to

Here, \(\Sigma_{\delta}(x)\) is the (co)variance calculated from the Gaussian process or polynomial regression model, and \(\sigma^2_{exp}\) is the maximum variance, taken over all configuration variables for that observation. In the case of field responses, the default prediction configurations are set equal to the input configurations, and the variance information contains only the variance calculated from the Gaussian process correction model. Further details can be found in the Dakota User’s and Theory Manuals.

*Usage Tips*

For field responses, the keyword
`read_field_coordinates`

<i>must</i> be specified when computing the model discrepancy. See
`field_calibration_terms`

for more information
regarding options for calibration with field responses.

*Expected Output*

The resulting values of
\(\delta(x_{new})\) will be exported to the file specified using
`export_discrepancy_file`

or to the default file
`dakota_discrepancy_tabular`

.dat. The values of the corrected model at the
specified prediction configurations will be exported to the file specified
using `export_corrected_model_file`

or to the default file
`dakota_corrected_model_tabular`

.dat, and the corresponding prediction
variances will be exported to `dakota_discrepancy_variance_tabular`

.dat
or the file specified with `export_corrected_variance_file`

.