Simultaneous model building and validation with uniform designs of experiments
Simultaneous model building and validation with uniform designs of experiments
复制标题
通过统一的实验设计同时进行模型构建和验证
DOI：
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发表时间：
2007
期刊：
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通讯作者：
I. F. Campean
中科院分区：
文献类型：
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作者：
A. Narayanan;V. Toropov;Alastair S. Wood;I. F. Campean
本文描述了一个特定的实验设计（DoE）计划的基础上，具有一定的空间填充和均匀性的最大化所获得的信息的目标的最佳拉丁超立方体的实现。这里强调的特征是同时模型构建和模型验证计划的概念，其联合包含与组件集相同的属性。两个拉丁超立方体能源部同时构建用于在元建模环境中的模型构建和模型验证。我们的目标是优化这两个集合的均匀性相对于设计的空间填充属性，同时满足合并的DoE的关键概念，包括构建和验证集的联合，具有类似的空间填充属性。这代表了第一次迭代（初始模型构建和验证）的最佳采样方法的开发，其中获得了大部分信息以充分利用并行计算。一个置换遗传算法，使用多种遗传算子策略的实施，其中适应度评价是基于Audze-Egais势能函数，并提出了一个例子，基于著名的六峰骆驼背函数。相对效率的策略和相关的计算方面进行了讨论相对于所获得的设计的质量。对这种设计方法的要求来自于需要在迭代多学科优化框架内多次调用传统上昂贵的系统和学科分析。
This article describes an implementation of a particular design of experiment (DoE) plan based upon optimal Latin hypercubes that have certain space-filling and uniformity properties with the goal of maximizing the information gained. The feature emphasized here is the concept of simultaneous model building and model validation plans whose union contains the same properties as the component sets. Two Latin hypercube DoE are constructed simultaneously for use in a meta-modelling context for model building and model validation. The goal is to optimize the uniformity of both sets with respect to space-filling properties of the designs whilst satisfying the key concept that the merged DoE, comprising the union of build and validation sets, has similar space-filling properties. This represents a development of an optimal sampling approach for the first iteration—the initial model building and validation where most information is gained to take the full advantage of parallel computing. A permutation genetic algorithm using several genetic operator strategies is implemented in which fitness evaluation is based upon the Audze-Eglais potential energy function, and an example is presented based upon the well-known six-hump camel back function. The relative efficiency of the strategies and the associated computational aspects are discussed with respect to the quality of the designs obtained. The requirement for such design approaches arises from the need for multiple calls to traditionally expensive system and discipline analyses within iterative multi-disciplinary optimisation frameworks.