Impact of Sampling Strategy on the Accuracy of Surrogate Models for Structural Analysis

Abstract

In modelling and simulation, approximations also known as metamodels, are commonly employed to approximate outcomes of complex simulations that are computationally intensive to evaluate. Various approximation techniques like response surface models, Kriging, radial basis functions, neural networks, etc. are utilized for surrogate modelling. These methods inherently rely on using a predefined dataset that forms the basis of training, validation and predictive capabilities of these surrogate models. Thus, the accuracy of these surrogate models is directly related to the quality of the underlying dataset used. Design of Experiments (DOE) methods are typically used to generate these datasets. The quality of the datasets is influenced by the choice of the DOE methods that are employed, as each DOE method uses a different strategy to sample the design space. Various sampling strategies like full factorial, fractional factorial, Latin hypercube sampling (LHS), Sobol sampling, etc. are available.

This study examines the impact of three sampling techniques- full factorial, fractional factorial, and Sobol sequencing on the accuracy and efficiency of surrogate models created using radial basis functions (RBF) for a structural analysis use-case.