  A computer model can be used for predicting an output only after specifying
the values of some unknown physical constants known as calibration parameters.
The unknown calibration parameters can be estimated from real data by
conducting physical experiments. This paper presents an approach to optimally
design such a physical experiment. The problem of optimally designing physical
experiment, using a computer model, is similar to the problem of finding
optimal design for fitting nonlinear models. However, the problem is more
challenging than the existing work on nonlinear optimal design because of the
possibility of model discrepancy, that is, the computer model may not be an
accurate representation of the true underlying model. Therefore, we propose an
optimal design approach that is robust to potential model discrepancies. We
show that our designs are better than the commonly used physical experimental
designs that do not make use of the information contained in the computer model
and other nonlinear optimal designs that ignore potential model discrepancies.
We illustrate our approach using a toy example and a real example from
industry.
