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Calibration of Stochastic Computer Models Using Stochastic Approximation Methods

机译:使用随机逼近方法校准随机计算机模型

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摘要

Computer models are widely used to simulate real processes. Within the computer model, there always exist some parameters which are unobservable in the real process but need to be specified in the model. The procedure to adjust these unknown parameters in order to fit the model to observed data and improve predictive capability is known as calibration. Practically, calibration is typically done manually. In this paper, we propose an effective and efficient algorithm based on the stochastic approximation (SA) approach that can be easily automated. We first demonstrate the feasibility of applying stochastic approximation to stochastic computer model calibration and apply it to three stochastic simulation models. We compare our proposed SA approach with another direct calibration search method, the genetic algorithm. The results indicate that our proposed SA approach performs equally as well in terms of accuracy and significantly better in terms of computational search time. We further consider the calibration parameter uncertainty in the subsequent application of the calibrated model and propose an approach to quantify it using asymptotic approximations.
机译:计算机模型被广泛用于模拟实际过程。在计算机模型内,总是存在一些在实际过程中无法观察到但需要在模型中指定的参数。调整这些未知参数以使模型适合观察数据并提高预测能力的过程称为校准。实际上,校准通常是手动完成的。在本文中,我们提出了一种基于随机逼近(SA)方法的有效且高效的算法,该算法可以轻松实现自动化。我们首先证明了将随机逼近应用于随机计算机模型校准的可行性,并将其应用于三个随机仿真模型。我们将我们提出的SA方法与另一种直接校准搜索方法,即遗传算法进行了比较。结果表明,我们提出的SA方法在准确性方面表现相同,并且在计算搜索时间方面明显更好。我们在校准模型的后续应用中进一步考虑了校准参数的不确定性,并提出了一种使用渐近近似对其进行量化的方法。

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