**Uncertainty Quantification Working Group**

May 17, 11:30 AM, CNLS Conf. Room

## Integrated Analysis of Computational and Physical Experimental Data

Shane Reese (D-1)

### Abstract

In scientific investigations, we frequently have data from computer
experiment(s) as well as related physical experimental data on the
same factors and related response variable(s). There may also exist
one or more expert opinions regarding the response of
interest. Traditional statistical approaches consider each of these
sets of data separately with corresponding separate analyses and
fitted statistical models. A compelling argument can be made that
better, more precise statistical models can be obtained if we
simultaneously analyze the combined data using a hierarchical Bayesian
integrated modeling approach. However, such an integrated approach
must recognize important differences, such as possible biases, in
these experiments and expert opinions. We illustrate the methodology
by using it to model the thermodynamic operation point of a top-spray
fluidized bed microencapsulation processing unit. Such units are used
in the food industry to tune the effect of functional ingredients and additives.
An important thermodynamic response variable of interest, *Y*, is the
steady-state outlet air temperature. In addition to a set of physical experimental
observations involving six factors used to predict *Y*, similar results from three
different computer models were also available. The integrated data from the
physical experiment and the three computer models are used to fit an
appropriate response surface (regression) model for predicting *Y*.

Send e-mail to author at reese@lanl.gov