Improving Search Efficiency in Engineering Design by Integrating Multiple Models at Different Fidelities
通过集成不同保真度的多个模型来提高工程设计中的搜索效率
基本信息
- 批准号:1462787
- 负责人:
- 金额:$ 45.91万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-05-01 至 2019-04-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The goal of this research is to improve search efficiency in engineering design. Engineering designers usually rely on predictive models to provide insight into which design alternatives to select. In this process, they must also decide which models to use, balancing model fidelity and computation cost. High-fidelity models usually provide a more accurate approximation but may be very costly and may require considerable computational resources. Low-fidelity models on the other hand tend to be much faster and can therefore be used to search through a large number of design alternatives in a short time. This award supports fundamental research to improve the search efficiency by integrating multiple models at different fidelities. The new model integration framework will enable engineering designers to combine the benefits of both fast and inexpensive low-fidelity models with accurate but more expensive high-fidelity models. Progress in this area will lead to the development of better engineered systems at a lower cost. The research will bridge several disciplines including engineering design, mathematical science and systems engineering, and contribute to the development of a new interdisciplinary curriculum enhancing the educational experience of engineering students.Although significant advances have been made in multiscale design and multidisciplinary design optimization, the theoretical foundation and practical approaches for integrating multiple models at different fidelities still remains to be investigated. This research will fill this knowledge gap by developing a theoretical and algorithmic framework with two key components: ordinal transformation and optimal sampling. The research will establish theoretical properties of a new ordinal design space created using a low-fidelity model and show that these properties facilitate efficient subsequent search for optimized designs. An optimal sampling method will then be developed to guide the search efficiently, at low-cost, taking into account both the information in the ordinal space and the bias in the low-fidelity model. The benefit of the extension to multiple low-fidelity models will also be investigated.
本研究的目的是提高工程设计中的搜索效率。工程设计人员通常依靠预测模型来提供选择设计方案的洞察力。在此过程中,他们还必须决定使用哪种模型,平衡模型保真度和计算成本。高保真度模型通常提供更精确的近似值,但可能非常昂贵,并且可能需要大量的计算资源。另一方面,低保真度模型往往要快得多,因此可以在短时间内搜索大量的设计方案。该奖项支持基础研究,通过整合不同保真度的多个模型来提高搜索效率。新的模型集成框架将使工程设计人员能够将快速和廉价的低保真模型的优点与精确但更昂贵的高保真模型结合起来。这一领域的进展将导致以更低的成本开发出更好的工程系统。这项研究将连结多个学科,包括工程设计、数学科学及系统工程,并有助于发展新的跨学科课程,提升工程学生的教育经验。尽管在多尺度设计和多学科设计优化方面取得了重大进展,但不同保真度下多模型集成的理论基础和实践方法仍有待研究。本研究将通过开发一个包含两个关键组件的理论和算法框架来填补这一知识空白:有序变换和最优采样。该研究将建立使用低保真度模型创建的新有序设计空间的理论性质,并表明这些性质有助于对优化设计的有效后续搜索。然后,考虑到有序空间中的信息和低保真模型中的偏差,将开发一种最优采样方法,以低成本有效地指导搜索。扩展到多个低保真模型的好处也将被研究。
项目成果
期刊论文数量(0)
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会议论文数量(0)
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Edward Huang其他文献
Capturing simulation tool and application domain knowledge for automating simulation model creation
获取仿真工具和应用领域知识以自动创建仿真模型
- DOI:
10.1057/jos.2014.9 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
O. Batarseh;Edward Huang;L. McGinnis - 通讯作者:
L. McGinnis
Modeling Inference Enterprises Using Multiple Interoperating Models
使用多种互操作模型对推理企业进行建模
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Kathryn B. Laskey;A. K. Zaidi;D. Buede;Muhammad Imran;Edward Huang;David P. Brown;B. Yousefi;Shou Matsumoto - 通讯作者:
Shou Matsumoto
Assessing transportation infrastructure impacts from supply chain restructuring for increased domestic production of critical resources
- DOI:
10.1016/j.cie.2023.109116 - 发表时间:
2023-04-01 - 期刊:
- 影响因子:
- 作者:
Qiang Chen;Elise Miller-Hooks;Edward Huang - 通讯作者:
Edward Huang
Do Public Subsidies Sell Green Cars? Evidence from the U.S. 'Cash for Clunkers' Program
公共补贴可以销售绿色汽车吗?
- DOI:
10.2139/ssrn.1591323 - 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Edward Huang - 通讯作者:
Edward Huang
A new optimal sampling rule for multi-fidelity optimization via ordinal transformation
通过序数变换实现多保真度优化的新最优采样规则
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Si Zhang;Jie Xu;Edward Huang;Chun - 通讯作者:
Chun
Edward Huang的其他文献
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