CDS&E: ECCS: Accurate and Efficient Uncertainty Quantification and Reliability Assessment for Computational Electromagnetics and Engineering

CDS

• 批准号: 2305106
• 负责人: Branislav Notaros
• 金额: $ 42万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305106&HistoricalAwards=false
• 关键词: CDS& ECCS; Accurate; Efficient; Uncertainty

Uncertainty quantification (UQ) permits analyses of sensitivity and reliability, which is of critical importance in all areas of engineering. Indeed, uncertainty is unavoidable in all engineering applications. Just as one example, UQ in biomedical computational electromagnetics (CEM) applications involves studies of electromagnetic field’s sensitivity to uncertainties in position and orientation of field exciters as well as dimensions and materials of biological objects. Through rigorous UQ, the effectiveness and reliability of analyses and designs may be improved drastically. With the growing demand for high-precision components, devices, and systems for consumer use (e.g., cellphones) or national security (e.g., stealth technology), the analysis of uncertainty is extremely important. In fact, in the design of practical systems and methods, the low-probability but high-risk events often dominate design concerns. To achieve these critical objectives, this project will conduct a cohesive analysis and treatment of deterministic and statistical errors to enhance engineering designs through automatic and rigorous techniques. The proposed methodology for error control and UQ in CEM and computational engineering enhances both quality and confidence in designs and simulation data while also increasing efficiency. Although the project focuses on safety-critical and mission-critical applications requiring high-quality rigorous UQ, the proposed new approach can also provide significant advantages in other CEM and numerical modeling applications. Compared to existing techniques facing challenges of severe limitations in the dimension of uncertain parameter space, the computational expense, and the ability to reliably and accurately model and calculate failure probabilities, particularly for high-risk events, the proposed approach has advantages of extensive adaptivity, achieving accuracy for high-dimensional problems, and computing the probabilities of arbitrary events rapidly. The project’s educational activities include advising and training of graduate students, recruiting students from underrepresented minority groups in STEM, developing new educational and course materials, and participating in various retention/outreach programs. The principal objective of this project is to formulate, develop, analyze, and demonstrate a novel synergistic approach of fully adaptive error control (both deterministic and statistical) and uncertainty quantification to greatly enhance the efficiency, accuracy, and usability of reliability assessment for engineering applications including electromagnetic systems and devices. The project develops a comprehensive approach to constrain deterministic error (to eliminate significant error propagation effects) and statistical error (to ensure high-quality resolution of success and failure probabilities). The novel approach promises significant savings in computational resources and enhanced performance for high-dimensional uncertainty. The approach will significantly improve the analysis of safety-critical and mission-critical problems demanding high-quality UQ. The analysis of uncertain events, particularly those with low-probability and high-risk, is untenable in practical applications through existing UQ approaches. Compared to existing methods, the proposed novel adaptive local resolution with dimension reduction UQ method has several unique features: (A) comprehensive deterministic and statistical error control synergy, as opposed to independent processes, to efficiently drive local resolution enhancements; (B) integral support for multiple objectives in the automated UQ processes with accelerated convergence to specified error tolerances; (C) novel adjoint-based similarity indicators to conduct significant efficiency enhancements through quantity of interest clustering; (D) high-resiliency to high-dimensional uncertainty while supporting multiple objectives and providing significantly enhanced convergence rates; (E) failure-probability-aware dimension reduction techniques through adjoint data indicators and parametric sensitivity metrics; and (F) identification of critical points in the parameter space to drive intelligent resource allocations and identify unstable regions. Overall, the proposed approach has a strong potential to fulfill the needs of rigorous, automatic, and efficient uncertainty quantification.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.

不确定性量化（UQ）允许对灵敏度和可靠性进行分析，这在所有工程领域都是至关重要的。事实上，不确定性在所有工程应用中都是不可避免的。举个例子，UQ在生物医学计算电磁学（CEM）中的应用涉及电磁场对场激子位置和方向的不确定性以及生物物体的尺寸和材料的敏感性的研究。通过严格的UQ，分析和设计的有效性和可靠性可能会大大提高。随着消费者（如手机）或国家安全（如隐身技术）对高精度组件、设备和系统的需求不断增长，对不确定性的分析非常重要。事实上，在实际系统和方法的设计中，低概率但高风险的事件往往主导着设计关注点。为了实现这些关键目标，该项目将对确定性和统计错误进行内聚分析和处理，通过自动化和严格的技术来增强工程设计。所提出的误差控制和UQ方法在CEM和计算工程中提高了设计和仿真数据的质量和信心，同时也提高了效率。虽然该项目侧重于需要高质量严格UQ的安全关键型和任务关键型应用，但所提出的新方法也可以在其他CEM和数值建模应用中提供显着优势。与现有方法在不确定参数空间维度、计算费用、可靠准确地建模和计算失效概率（特别是高风险事件）等方面面临的挑战相比，该方法具有广泛的自适应性、对高维问题的准确性和对任意事件概率的快速计算等优点。该项目的教育活动包括为研究生提供咨询和培训，从STEM中代表性不足的少数群体中招募学生，开发新的教育和课程材料，以及参与各种保留/外展计划。该项目的主要目标是制定、开发、分析和演示一种新的全自适应误差控制（确定性和统计）和不确定性量化的协同方法，以大大提高包括电磁系统和设备在内的工程应用的可靠性评估的效率、准确性和可用性。该项目开发了一种全面的方法来约束确定性误差（以消除显著的误差传播效应）和统计误差（以确保高质量地解决成功和失败概率）。这种新方法有望显著节省计算资源，并提高高维不确定性的性能。该方法将显著提高对需要高质量UQ的安全关键问题和任务关键问题的分析。通过现有的UQ方法，对不确定事件，特别是低概率和高风险事件的分析在实际应用中是站不住脚的。与现有的自适应局部分辨率降维UQ方法相比，该方法具有以下几个独特的特点：(A)综合的确定性和统计误差控制协同作用，而不是独立的过程，有效地驱动了局部分辨率的增强；(B)集成支持自动化UQ过程中的多个目标，加速收敛到指定的误差容限；(C)新颖的基于伴随的相似性指标，通过兴趣数量聚类显著提高效率；(D)对高维不确定性的高弹性，同时支持多个目标并提供显著提高的收敛速度；(E)通过伴随数据指标和参数灵敏度度量的故障概率感知降维技术；(F)识别参数空间中的关键点，驱动智能资源分配，识别不稳定区域。总的来说，所提出的方法在满足严格、自动和高效的不确定性量化需求方面具有很强的潜力。该奖项反映了美国国家科学基金会的法定使命，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。