Multi-fidelity Accelerated Global Search (MAGS)

多保真加速全局搜索 (MAGS)

• 批准号: 2204872
• 负责人: Zelda Zabinsky
• 金额: $ 42.09万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204872&HistoricalAwards=false
• 关键词: Multi; fidelity; Accelerated; Global; Search

This award will promote the progress of science and contribute to the advancement of national prosperity and health by introducing new computational methods to guide the design and control of modern manufacturing facilities, such as complex bio-manufacturing processes that produce personalized medical treatments. High-fidelity simulation models that can be used to evaluate the performance of a manufacturing system are computationally intensive and often require expensive data collection, limiting their use. On the other hand, queueing models use less computation time but are less accurate. There is a substantial gap in understanding how to effectively make use of both high- and low-fidelity models to design and control such systems. This award supports advancing theoretical foundations and algorithm development for large-scale global optimization enabling the use of multiple models of varying accuracy and computational effort. This research will have broad impact on the growth of bio-manufacturing in the U.S., and will provide a diverse population of students with the education and training needed to deploy and extend these tools, advancing the national economic welfare.This project will create a new Multi-fidelity Accelerated Global Search (MAGS) framework for stochastic global optimization that will intelligently combine high- and low-fidelity models to dynamically allocate computational effort guided by statistical analysis of solution quality. The goal is to reduce the number of high-fidelity simulation evaluations by taking advantage of low-fidelity models, thus integrating model approximation (learning) with optimization (search). This research addresses algorithmic design, scalability, and finite-time stochastic analysis of MAGS. The project will provide a theoretical foundation for computational complexity and aid in deriving a dynamic and adaptable decomposition scheme to realize large-scale optimization. The design and operation of an individualized bio-manufacturing system will be used to test and refine MAGS throughout its development.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.

该奖项将通过引入新的计算方法来指导现代制造设施的设计和控制，例如生产个性化医疗的复杂生物制造过程，从而促进科学进步，并为促进国家繁荣和健康做出贡献。可用于评估制造系统性能的高保真仿真模型计算密集，通常需要昂贵的数据收集，限制了它们的使用。另一方面，排队模型使用较少的计算时间，但精确度较低。在理解如何有效地利用高保真和低保真模型来设计和控制这类系统方面存在着很大的差距。该奖项支持为大规模全球优化提供先进的理论基础和算法开发，从而能够使用不同精度和计算工作量的多个模型。这项研究将对美国生物制造业的发展产生广泛的影响，并将为不同的学生群体提供部署和推广这些工具所需的教育和培训，促进国民经济福祉。该项目将创建一个新的多保真加速全球搜索(MAGS)框架，用于随机全局优化，该框架将智能地结合高保真和低保真模型，在解决方案质量的统计分析指导下动态分配计算工作量。其目标是通过利用低保真模型来减少高保真模拟评估的数量，从而将模型近似(学习)与优化(搜索)相结合。这项研究涉及MAGS的算法设计、可伸缩性和有限时间随机分析。该项目将为计算复杂性提供理论基础，并有助于推导出动态和自适应的分解方案，以实现大规模优化。个性化生物制造系统的设计和运行将用于在整个发展过程中测试和提炼MAG。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。