  Tuning a complex simulation code refers to the process of improving the
agreement of a code calculation with respect to a set of experimental data by
adjusting parameters implemented in the code. This process belongs to the class
of inverse problems or model calibration. For this problem, the approximated
nonlinear least squares (ANLS) method based on a Gaussian process (GP)
metamodel has been employed by some researchers. A potential drawback of the
ANLS method is that the metamodel is built only once and not updated
thereafter. To address this difficulty, we propose an iterative algorithm in
this study. In the proposed algorithm, the parameters of the simulation code
and GP metamodel are alternatively re-estimated and updated by maximum
likelihood estimation and the ANLS method. This algorithm uses both computer
and experimental data repeatedly until convergence. A study using toy-models
including inexact computer code with bias terms reveals that the proposed
algorithm performs better than the ANLS method and the
conditional-likelihood-based approach. Finally, an application to a nuclear
fusion simulation code is illustrated.
