Uncertainty analysis is an essential procedure to evaluate reliability or robustness in uncertainty-based multidisciplinary optimization. Considering non-probabilistic interval uncertainties, this paper proposes a trust region-based sequential radial basis function (TR-SRBF) method for interval uncertainty analysis of multidisciplinary systems. First, the radial basis function neural network (RBFNN) is introduced to establish the correlation model between uncertain parameters and multidisciplinary outputs. After training a crude RBFNN via a small number of initial sample points, the proposed method sequentially collects sample points and updates the surrogate model according to the current accuracy. A trust region-based updating scheme is established to determine the sampling areas and guide the collection of new sample points. After successively updating, a satisfactory surrogate model will be obtained, based on which the extrema of multidisciplinary outputs can be obtained conveniently with some auxiliary algorithms. Further, to reduce the sample size, an alternant scheme is then presented to calculate the lower and upper bounds of the multidisciplinary outputs simultaneously. Finally, numerical examples are provided to demonstrate the effectiveness and applicability of TR-SRBF. By contrast with the static surrogate-based method, the results show that the proposed method can achieve better efficiency as well as high accuracy. The main contribution of this paper is to provide a novel dynamic surrogate-based interval uncertainty analysis method called TR-SRBF to calculate the upper and lower bounds of multidisciplinary outputs, in which the RBFNN is sequentially updated with the proposed trust region-based sampling scheme while the bounds are alternately calculated.

不确定性分析是在基于不确定性的多学科优化中评估可靠性或稳健性的基本程序。考虑到非概率区间不确定性，本文提出了一种基于信任域的序列径向基函数（TR-SRBF）方法，用于多学科系统的区间不确定性分析。首先，引入径向基函数神经网络（RBFNN）建立不确定参数与多学科输出之间的相关模型。在通过少量初始样本点训练一个粗略的 RBFNN 之后，所提出的方法依次收集样本点并根据当前精度更新代理模型。建立基于信任域的更新方案来确定采样区域并指导新样本点的收集。经过不断的更新，会得到一个满意的代理模型，在此基础上通过一些辅助算法可以方便地得到多学科输出的极值。此外，为了减少样本量，然后提出了一种交替方案来同时计算多学科输出的下限和上限。最后，提供了数值例子来证明 TR-SRBF 的有效性和适用性。与基于静态代理的方法相比，结果表明所提出的方法可以实现更好的效率和高精度。本文的主要贡献是提供了一种新的基于动态代理的区间不确定性分析方法，称为 TR-SRBF 来计算多学科输出的上下界，