CAREER: Efficient Predictive Modeling for Infrastructure Systems Using Polynomial Approximation

基础设施系统的健康和优化运行将促进社会的健康、繁荣和福利。然而，考虑到这些系统中涉及的各种不确定性，保持最优状态是具有挑战性的。计算和传感技术的进步可以通过现实地捕捉涉及的不确定性，潜在地实现基础设施系统管理的范式转变。主要的挑战仍然是高昂的计算成本，这主要是因为基础设施网络的规模很大。该学院早期职业发展计划(Career)奖项旨在支持下一代快速不确定性量化(UQ)方法，这些方法可以显著减少模拟时间，并特别为大型基础设施网络量身定制。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。该项目还将制定一项综合教育和外联计划，以培养具有更好的编程和计算技能的下一代土木工程师。这是通过开发一门新的研究生水平的课程，指导本科生研究人员，与教育生理学家合作来改进教育计划，以及向更广泛的土木工程学生群体、K-12学生和决策者拓展。新方法将促进城市生活质量和基础设施建模方面的科学进步，与教育和外联活动一起，可以共同改善基础设施的运作，促进我们社会的经济竞争力。这个项目将使用随机模拟来真实地捕捉复杂性。这些办法将在现有UQ机制的基础上发展和丰富。这些先进的UQ方法旨在(1)智能地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进行模型约简，(3)建立使用流传感器数据的有效在线训练框架，以及(4)利用各种保真度水平的流模型的可用性来产生多保真预测。具体地说，这些方法包括：用于有效消除与网络冗余相关的不确定性的多项式混沌展开中的高级压缩采样方法；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；分块递归最小二乘方法，以使具有量化建模误差的多项式代理能够有效地在线学习；以及多保真回归框架，用于使用不同保真度水平的模拟结果来构建代理。如果成功，这些进展将共同促进基础设施工程的转型，在基础设施工程中，将越来越多地使用基于模拟的数据密集型设计套件，以帮助决策者。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。

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.