Predictive Estimation of Model Fidelity (PEMF) is a model-independent approach to measure the fidelity of surrogate models or metamodels, such as Kriging, Radial Basis Functions (RBF), Support Vector Regression (SVR), and Neural Networks.
Cite PEMF as:
A. Mehmani, S. Chowdhury, and A. Messac, “Predictive quantification of surrogate model fidelity based on modal variations with sample density,” Structural and Multidisciplinary Optimization, vol. 52, pp. 353-373, 2015.
This is the first release of PEMF; so, any feedback or information on any bugs (that we might have missed) is very welcome.