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When experimentation on a real system is expensive, data are often collected by using cheaper, lower fidelity surrogate systems. The paper concerns response surface methods in the context of variable fidelity experimentation. We propose the use of generalized least squares to generate the predictions. We also present perhaps the first optimal designs for variable fidelity experimentation, using an extension of the expected integrated mean-squared error criterion. Numerical tests are used to compare the performance of the method with alternatives and to investigate the robustness to incorporated assumptions. The method is applied to automotive engine valve heat treatment process design in which real world data were mixed with data from two types of computer simulation. Copyright 2005 Royal Statistical Society.

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