Title: Variable-fidelity models in optimization of simulation-based
systems 

Many physical phenomena in engineering design can be described by
computational models of high physical fidelity or numerical accuracy.
Optimization with high-fidelity simulations gives rise to large-scale
nonlinear programming problems (NLP) due to the large number of state
variables and the associated computational cost of solving (coupled)
differential equations that govern the behavior of the system.
Straightforward use of high-fidelity models, such as the Navier-Stokes
equations or those based on fine computational meshes, in iterative
procedures can be prohibitively expensive. We discuss a first-order, 
variable-fidelity model management scheme for solving optimization 
problems whose function evaluations involve outputs of simulations. 
The approach reduces the cost of optimization by systematically using 
lower-fidelity models or surrogates, with occasional recourse to 
high-fidelity computations for model re-calibration.  Convergence to 
high-fidelity results is maintained. We discuss computation with
variable-resolution and variable physical fidelity models, as well 
as a variety of response surface approximations.