A Comparative Study on Accuracy and Efficiency of Metamodels for Large Industrial Datasets

This paper provides a comparative study on accuracy and efficiency of metamodels constructed from large datasets. Two examples inspired by large industrial applications are used to identify the best metamodeling technique. Artificial Neural Networks, Radial Basis Functions, Gaussian Process and Nonlinear regression are used to build metamodels. The examples used showcase a broad range of industrial applications in aircraft engines and gas turbines. Although Radial Basis Functions and Gaussian Process models are robust for small data sets, their high computational cost for large datasets reduces their practical application. ANN models are found to perform optimally when large number of training points are readily available and the accuracy requirements are high.