The Morris method is an effective sample-based sensitivity analysis technique that has been applied in various disciplines. To ensure a more proper coverage of the input space and better performance, an enhanced framework for Morris is proposed by considering the combination of a sequential sampling strategy and the traditional Morris method. The paper introduces utilizing progressive Latin hypercube sampling to generate starting points while progressively preserving Latin hypercube property. Then the calculations for Elementary Effects, which occupies the major computational cost of Morris, become sequential. An adaptive stop criterion is also constructed to end the algorithm when the convergence condition is satisfied. Therefore, the proposed procedure makes the cost of Morris more manageable and minimizes the computational burden by conducting only model runs that are necessary to achieve reliable results. Two numerical examples and two real-world cases are given to illustrate the effectiveness and robustness of the framework.

中文翻译：

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结合顺序采样策略的 MORRIS 增强框架

Morris 方法是一种有效的基于样本的敏感性分析技术，已应用于各个学科。为了确保更适当地覆盖输入空间和更好的性能，通过考虑顺序采样策略和传统 Morris 方法的组合，提出了 Morris 的增强框架。本文介绍了利用渐进式拉丁超立方体采样来生成起点，同时渐进式保留拉丁超立方体属性。然后，占据 Morris 主要计算成本的 Elementary Effects 的计算变得连续。当满足收敛条件时，还构造了自适应停止准则以结束算法。所以，所提出的程序使 Morris 的成本更易于管理，并通过仅进行获得可靠结果所必需的模型运行来最小化计算负担。给出了两个数值示例和两个真实案例来说明该框架的有效性和稳健性。