CAREER: Efficient Predictive Modeling for Infrastructure Systems Using Polynomial Approximation

职业：使用多项式逼近对基础设施系统进行高效预测建模

基本信息

批准号：
1752302
负责人：
Hadi Meidani
金额：
$ 50万
依托单位：
University of Illinois at Urbana-Champaign
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：
2018-03-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1752302&HistoricalAwards=false
关键词：
CAREER Efficient Predictive Modeling Infrastructure

项目摘要

Healthy and optimal operation of infrastructure systems will advance the health, prosperity and welfare of the society. It is however challenging to maintain the optimal condition given various uncertainties involved in these systems. Advances in computing and sensing technologies can potentially enable a paradigm shift in the management of infrastructure systems by realistically capturing the uncertainties involved. The main challenge is still the high computational cost that would entail, mainly due to large size of infrastructure networks. This Faculty Early Career Development Program (CAREER) award aims to enable the next generation of fast uncertainty quantification (UQ) methodologies that can significantly reduce simulation time and are particularly tailored for large infrastructure networks. This award will analyze the case of interdependent transportation-energy systems that involves the integration of electric vehicles. The project will also establish an integrated education and outreach plan to prepare the next generation of civil engineers with improved programming and computational skills. This is done through development of a new graduate-level course, mentoring of undergraduate researchers, partnership with educational physiologists to enhance educational plans, and also outreach to the broader civil engineering student population, K-12 students, and decision makers. The new methods will promote progress of science in UQ and infrastructure modeling, and together with the educational and outreach activities can collectively lead to improved infrastructure operations and promote the economic competitiveness of our society. This project will use stochastic simulations to realistically capture the complexities. The approaches will build upon and enrich current UQ machinery. These advanced UQ methods will aim to (1) intelligently identify the redundancies in the flow models of infrastructure systems, (2) use topology characteristics of these networks towards model reduction, (3) build an effective online training framework that use streaming sensor data, and (4) take advantage of the availability of flow models at various fidelity levels to produce multifidelity predictions. Specifically, the approaches include advanced compressive sampling methods in polynomial chaos expansion for effective removal of uncertainties related to network redundancies; a topology-based dimension reduction algorithm integrated with compressive sampling approach to exploit the topology information of infrastructure networks; a block-wise recursive least square approach to enable an effective online learning for polynomial surrogates with quantified modeling errors; and a multifidelity regression framework for building surrogates using results of simulations at various fidelity levels. If successful, these advances will collectively contribute to the transformation of infrastructure engineering where simulation-based data-intensive design suites that can assist decision makers will be increasingly used.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.

基础设施系统的健康、优化运行将促进社会的健康、繁荣和福祉。然而，考虑到这些系统涉及的各种不确定性，保持最佳状态具有挑战性。计算和传感技术的进步可以通过实际捕获所涉及的不确定性来实现基础设施系统管理的范式转变。主要挑战仍然是所需的高计算成本，这主要是由于基础设施网络规模庞大。该学院早期职业发展计划 (CAREER) 奖项旨在实现下一代快速不确定性量化 (UQ) 方法，该方法可以显着减少仿真时间，并且特别适合大型基础设施网络。该奖项将分析涉及电动汽车集成的相互依赖的交通能源系统的案例。该项目还将制定综合教育和推广计划，以提高下一代土木工程师的编程和计算技能。这是通过开发新的研究生课程、指导本科生研究人员、与教育生理学家合作以加强教育计划以及向更广泛的土木工程学生群体、K-12 学生和决策者进行推广来实现的。新方法将促进昆士兰大学和基础设施建模的科学进步，并与教育和推广活动一起共同改善基础设施运营并提高我们社会的经济竞争力。该项目将使用随机模拟来真实地捕捉复杂性。这些方法将建立在昆士兰大学现有机制的基础上并丰富其内容。这些先进的昆士兰大学方法旨在（1）智能识别基础设施系统流模型中的冗余，（2）利用这些网络的拓扑特征来减少模型，（3）构建一个使用流传感器数据的有效在线培训框架， (4) 利用各种保真度级别的流模型的可用性来产生多保真度预测。具体来说，这些方法包括多项式混沌扩展中的先进压缩采样方法，以有效消除与网络冗余相关的不确定性；基于拓扑的降维算法与压缩采样方法相结合，以利用基础设施网络的拓扑信息；分块递归最小二乘法，可以对具有量化建模误差的多项式替代项进行有效的在线学习；以及使用各种保真度级别的模拟结果构建代理的多保真度回归框架。如果成功，这些进步将共同促进基础设施工程的转型，其中将越来越多地使用可以帮助决策者的基于模拟的数据密集型设计套件。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。