When Does a Diverse Initial Solution Set Lead to Better Engineering Design Outcomes?

多样化的初始解决方案何时会带来更好的工程设计成果？

• 批准号: 1826083
• 负责人: Mark Fuge
• 金额: $ 54.94万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826083&HistoricalAwards=false
• 关键词: When; Does; Diverse; Initial; Solution

Designing high-performing engineering systems--for example, fuel-efficient aircraft, medical devices, new manufacturing and agricultural equipment--requires searching for high-quality solutions among many possible options. It can be difficult to find good solutions to complex problems, and one rule-of-thumb people use is to first start with a diverse set of options. The intuition is that having diverse starting points will make it more likely that one will eventually find a good final solution. But is this always the case? For what types of problems does having diverse initial solutions help versus hinder creating better final solutions? This award supports fundamental research into mathematical models that answer these questions. Specifically, this research studies new models that 1) map how engineering problems differ from each other in terms of their structure and the processes people use to solve them, and 2) predict whether diverse initial solutions across each type of engineering problem lead to a higher quality final solution. The work will impact society by providing a way to reduce costs and improve problem-solving performance of engineers across multiple industries. The mathematical models will also provide foundational understanding to other fields where similar questions arise, such as in biology, computer science, and physics.The technical objectives of this project are to (1) unify various representations of engineering systems through the use of Category Theory and isomorphisms between types of solution spaces; (2) encode search and design processes using Random Matrix Theory to track solutions over that unified representation; (3) use metrics from Submodular Vertex Coverage to calculate the diversity of initial and final solution sets; (4) test the model across five benchmark problems of increasing difficulty and complexity, using computational simulations and behavioral experiments with human participants; and (5) study the finite sample behavior of the model using Matrix Concentration Inequalities. The results from this project will advance the field of engineering by providing a testable model for how diverse initial solutions impact a wide range of tasks from human decision making and teaming to computational optimization algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

设计高性能的工程系统-例如省油的飞机、医疗设备、新的制造和农业设备-需要在许多可能的选择中寻找高质量的解决方案。对于复杂的问题，很难找到好的解决方案，人们使用的一个经验法则是首先从一组不同的选项开始。直觉是，有不同的出发点将使它更有可能最终找到一个好的最终解决方案。但情况总是这样吗？对于哪些类型的问题，不同的初始解决方案有助于而不是阻碍创建更好的最终解决方案？该奖项支持对回答这些问题的数学模型的基础研究。具体来说，这项研究研究的新模型，1）映射工程问题在结构和人们用来解决它们的过程方面如何彼此不同，2）预测每种类型的工程问题的不同初始解决方案是否会导致更高质量的最终解决方案。这项工作将通过提供一种降低成本和提高多个行业工程师解决问题能力的方法来影响社会。数学模型也将为其他领域提供基础性的理解，在类似的问题出现，如在生物学，计算机科学和物理学。这个项目的技术目标是（1）统一各种表示的工程系统通过使用范畴理论和同构之间的类型的解决方案空间;（2）使用随机矩阵理论对搜索和设计过程进行编码，以在该统一表示上跟踪解决方案;（3）使用来自子模块顶点覆盖的度量来计算初始和最终解决方案集的多样性;（4）使用计算模拟和人类参与者的行为实验，在难度和复杂性不断增加的五个基准问题上测试模型;（5）使用矩阵集中不等式研究模型的有限样本行为。该项目的成果将通过提供一个可测试的模型来推动工程领域的发展，该模型可以说明不同的初始解决方案如何影响从人类决策和团队合作到计算优化算法的广泛任务。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

How Diverse Initial Samples Help and Hurt Bayesian Optimizers

多样化的初始样本如何帮助和损害贝叶斯优化器

• DOI: 10.1115/1.4063006
• 发表时间: 2023
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Kamrah, Eesh;Ghoreishi, Seyede Fatemeh;Ding, Zijian “Jason”;Chan, Joel;Fuge, Mark
• 通讯作者: Fuge, Mark