CAREER: New Foundations for Multi-Fidelity Prediction, Estimation, and Learning Under Uncertainty in Dynamical Systems

职业生涯：动态系统不确定性下多保真度预测、估计和学习的新基础

• 批准号: 2238913
• 负责人: Alex Gorodetsky
• 金额: $ 72.1万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238913&HistoricalAwards=false
• 关键词: CAREER; New; Foundations; Multi; Fidelity

This Faculty Early Career Development (CAREER) grant will fund research that enables autonomous systems to estimate the effects of prediction uncertainty on planning and control decisions, with application to autonomous flight of soaring aircraft and in urban environments, thereby promoting the progress of science and advancing the national prosperity. Autonomous soaring aircraft offer new fuel-efficient approaches for travel, reconnaissance, and observation, including in hard-to-reach areas of the atmosphere. Their built-in simulators make assumptions about the presence of atmospheric boundary layers and updrafts to optimally extract energy from the prevailing winds. Computational models are also integrated in the planning and control architecture of unmanned aerial vehicles as they predict optimal paths through urban infrastructure based on estimates of the surrounding flow fields. Without assessing the uncertainty in their predictions, such simulators may result in suboptimal or catastrophic decisions, as opportunities for optimal performance are missed or safety constraints are violated. This project addresses this challenge by developing new, fast, and automated algorithms for rigorously quantifying uncertainty and updating computational models accordingly, and by validating these algorithms using experimental aircraft in controlled but complex wind conditions. The research is integrated with educational efforts aiming to bring a computational perspective on modeling, data science, and statistics to engineering students and the public through a series of workshops built around relevant case studies, a new data science class for an aerospace engineering curriculum, and a partnership with the Ann Arbor Hands-On Museum to develop exhibits accessible to K-8 students and their parents.This research aims to develop the foundations of automated approaches for deriving problem-specific multi-fidelity uncertainty quantification techniques. Such techniques aim to fuse information from simulation and data sources of varying fidelity and cost to achieve accurate predictions at a significantly lower computational cost than that required by the highest fidelity model. Current realizations are based on heuristics that are not adapted to the specific relationships between data sources of a given problem. To overcome this limitation, the research will derive new statistical estimators through analysis of Bayesian posteriors and maximum entropy distributions endowed with only the information available rather than heuristics; use these estimators to develop new filtering, state estimation, and Bayesian inference techniques; and demonstrate how these techniques may be applied to challenging nonlinear, chaotic, and non-Gaussian dynamical systems arising in the context of planning and control of soaring aircraft in complex wind fields.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.

该学院早期职业发展（CAREER）拨款将资助研究，使自主系统能够估计预测不确定性对规划和控制决策的影响，并应用于飙升飞机和城市环境中的自主飞行，从而促进科学进步和促进国家繁荣。自主翱翔飞机为旅行、侦察和观察提供了新的省油方法，包括在大气层难以到达的区域。他们的内置模拟器对大气边界层和上升气流的存在做出假设，以最佳方式从盛行风中提取能量。计算模型也被集成在无人驾驶飞行器的规划和控制架构中，因为它们根据对周围流场的估计来预测通过城市基础设施的最佳路径。如果不评估其预测的不确定性，这些模拟器可能会导致次优或灾难性的决策，因为错过了最佳性能的机会或违反了安全约束。该项目通过开发新的、快速的自动化算法来应对这一挑战，这些算法用于严格量化不确定性并相应地更新计算模型，并通过在受控但复杂的风况下使用实验飞机来验证这些算法。该研究与教育工作相结合，旨在通过围绕相关案例研究，航空航天工程课程的新数据科学课程，并与安阿伯动手博物馆合作，开发K-8名学生和他们的父母。这项研究的目的是开发基础的自动化方法，以获得特定问题的多保真度不确定性量化技术。这些技术旨在融合来自不同保真度和成本的模拟和数据源的信息，以比最高保真度模型所需的计算成本低得多的计算成本实现准确的预测。目前的实现是基于不适合于给定问题的数据源之间的特定关系的逻辑学。为了克服这一局限性，本研究将通过对贝叶斯后验和最大熵分布的分析，得到新的统计估计量，并利用这些估计量发展新的滤波、状态估计和贝叶斯推理技术;并演示了如何将这些技术应用于具有挑战性的非线性，混沌，该奖项反映了NSF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。