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Performance of surrogate models in reliability-based design optimization

机译：基于可靠性的设计优化的代理模型的性能

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Structural designs are progressively more conditioned by uncertainty in a wide range of fields, and new designs have to meet the requirements of safety and efficiency. Probabilistic optimization is a powerful tool able to improve and optimize initial designs into others which are more efficient. In spite of the continuous growth of computational power, this kind of optimization usually turns out to be unaffordable, due to the computational cost of the methods involved. However, parallel computing and surrogate models can overcome this drawback by reducing drastically the total computational cost of the process. In this paper, a survey of the performance of several surrogate models combined with reliability-based design optimization is presented. Two examples are selected to show the behavior of the methods when applied to different models.
机译：结构设计在广泛的领域中的不确定性逐渐变得更加调节，并且新设计必须满足安全性和效率的要求。概率优化是一种能够改进和优化初始设计的强大工具，进入更有效的初始设计。尽管计算能力的持续增长，但由于所涉及的方法的计算成本，这种优化通常是不稳定的。然而，并行计算和代理模型可以通过急剧减少过程的总计算成本来克服这一缺点。本文提出了一种与基于可靠性的设计优化相结合的几种替代模型的性能调查。选择两个示例以显示应用于不同模型时方法的行为。

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