论文标题
一种创建合成多保真数据集的原则性方法
A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets
论文作者
论文摘要
在许多计算设计领域中，多因素和多输出优化算法具有积极的兴趣，因为它们允许廉价的计算代理智能地使用，以帮助对高性能物种进行实验性搜索。这些算法的表征涉及通常使用分析功能或现有多重元素数据集的基准测试。但是，分析功能通常不能代表相关问题，而先前存在的数据集则不允许系统地研究较低忠诚度代理的特征的影响。为了弥合这一差距，我们提出了一种系统生成的方法论，该方法是从先前存在的数据集中得出的合成保真度。这允许构建基准，这些基准都代表了实际优化问题，同时还可以系统地研究较低的忠诚度代理的影响。
Multifidelity and multioutput optimisation algorithms are of active interest in many areas of computational design as they allow cheaper computational proxies to be used intelligently to aid experimental searches for high-performing species. Characterisation of these algorithms involves benchmarks that typically either use analytic functions or existing multifidelity datasets. However, analytic functions are often not representative of relevant problems, while preexisting datasets do not allow systematic investigation of the influence of characteristics of the lower fidelity proxies. To bridge this gap, we present a methodology for systematic generation of synthetic fidelities derived from preexisting datasets. This allows for the construction of benchmarks that are both representative of practical optimisation problems while also allowing systematic investigation of the influence of the lower fidelity proxies.
