Abstract
Computer experiments are widely used to evaluate the performance and reliability of engineering systems with the lowest possible time and cost. Sometimes, a high-fidelity model is required to ensure predictive accuracy; this becomes computationally intensive when many computational analyses are required (for example, inverse analysis or uncertainty analysis). In this context, a surrogate model can play a valuable role in addressing computational issues. Surrogate models are fast approximations of high-fidelity models. One efficient way for surrogate modeling is the sequential sampling (SS) method. The SS method sequentially adds samples to refine the surrogate model. This paper proposes a multiple-update-infill sampling method using a minimum energy design to improve the global quality of the surrogate model. The minimum energy design was recently developed for global optimization to find multiple optima. The proposed method was evaluated with other multiple-update-infill sampling methods in terms of convergence, accuracy, sampling efficiency, and computational cost.
Highlights
The traditional development of an engineering system requires repeated experiments involving evaluation of variability in material properties and external conditions
This paper proposes a multiple-update-infill sampling method using a minimum energy design (MED) to improve the global quality of the surrogate model
This study considers four performance measures as follows: (1) root mean squared error (RMSE), (2) minimum distance, (3) pairwise correlation (ρ2 ), and (4) computational efficiency (Tnorm )
Summary
The traditional development of an engineering system requires repeated experiments involving evaluation of variability in material properties and external conditions (for example, loading and excitation) Such development is a time-consuming and cost-expensive development process undertaken with limited resources, and some experiments are infeasible due to technical limitations (for example, extreme loadings such as earthquakes and collisions). In this regard, computer experiments, using the lowest amount of time and the lowest cost possible, are widely used to evaluate the performance reliability of engineering systems [1]. Applications using high-fidelity models become computationally expensive and infeasible under limited resources
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