A method is presented to integrate computational and experimental data sets, allowing development of an accurate and comprehensive model of a system response surface. The method is derived from Generalized Tikhonov Regularization for ill-posed problems. Through several numerical examples, the application of the new method to perform data fusion is demonstrated. The results show that a priori computational models may be improved by integrating experimental or computational data from other sources. The results also demonstrate the ability of the method to use an a priori model to smoothly interpolate sparse, noisy data. The method is compared to an earlier iterative approach for determining the regularization parameter. The limitations of the methodology in certain problem formulations are examined and suggestions for future work are described.