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Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach

机译:考虑保真度和鲁棒性的不精确和不精确数值模型的校准:基于多目标优化的方法

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摘要

Traditionally, model calibration is formulated as a single objective problem, where fidelity to measurements is maximized by adjusting model parameters. In such a formulation however, the model with best fidelity merely represents an optimum compromise between various forms of errors and uncertainties and thus, multiple calibrated models can be found to demonstrate comparable fidelity producing non-unique solutions. To alleviate this problem, the authors formulate model calibration as a multi-objective problem with two distinct objectives: fidelity and robustness. Herein, robustness is defined as the maximum allowable uncertainty in calibrating model parameters with which the model continues to yield acceptable agreement with measurements. The proposed approach is demonstrated through the calibration of a finite element model of a steel moment resisting frame.
机译:传统上,模型校准被表述为一个单一的目标问题,其中通过调整模型参数使测量的保真度最大化。然而,在这样的表述中,具有最佳保真度的模型仅代表了各种形式的误差和不确定性之间的最佳折衷,因此,可以找到多个校准模型来证明可比较的保真度产生非唯一解。为了缓解这个问题,作者将模型校准公式化为具有两个不同目标的多目标问题:保真度和鲁棒性。在本文中,鲁棒性定义为校准模型参数时允许的最大不确定性,通过该最大不确定性,模型可以继续获得可接受的测量结果一致性。通过对钢制抗弯框架的有限元模型的校准,证明了所提出的方法。

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