Bayesian updating with subset simulation using artificial neural networks
Bayesian updating with subset simulation using artificial neural networks
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DOI：
10.1016/j.cma.2017.02.025
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发表时间：
2017-06-01
影响因子：
7.2
通讯作者：
Papadopoulos, Vissarion
中科院分区：
文献类型：
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作者：
Giovanis, Dimitris G.;Papaioannou, Iason;Papadopoulos, Vissarion
我们提出了一种混合的方法，实现人工神经网络（ANN）的框架内的贝叶斯更新与结构可靠性方法（BUS），以提高计算效率的BUS在基于抽样的贝叶斯推理的数值模型。特别是，人工神经网络被纳入总线与子集模拟（SuS）。基本概念是在SuS的每个子集中训练ANN，每个子集具有所需数量的样本的一部分，并采用训练的ANN来生成剩余的样本。这是通过在SuS内的马尔可夫链蒙特卡罗（MCMC）模拟的候选样本点处由ANN估计替换完整模型评估来实现的。为了确保代理的准确性，每个ANN估计值都针对一组条件进行测试。人工神经网络训练是专门针对使用MCMC增强的BUS的自适应变体进行的，具有最佳扩展性。通过三个算例的数值结果验证了该方法的有效性和适用性。(C)2017爱思唯尔B.V.保留所有权利。
We propose a hybrid methodology that implements artificial neural networks (ANN) in the framework of Bayesian updating with structural reliability methods (BUS) in order to increase the computational efficiency of BUS in sampling-based Bayesian inference of numerical models. In particular, ANNs are incorporated in BUS with subset simulation (SuS). The basic concept is to train an ANN in each subset of SuS with a fraction of the required number of samples per subset and employ the trained ANN to generate the remaining samples. This is achieved by replacing the full model evaluation at a candidate sample point of the Markov Chain Monte Carlo (MCMC) simulation within SuS by an ANN estimate. To ensure the accuracy of the surrogate, each ANN estimate is tested against a set of conditions. The ANN training is specifically tailored to the adaptive variant of BUS enhanced with MCMC with optimal scaling. The applicability as well as the efficiency of the proposed method are examined by means of numerical results in three test cases. (C) 2017 Elsevier B.V. All rights reserved.