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.

这种教师早期职业发展（职业）赠款将资助研究，使自主系统能够估算预测不确定性对计划和控制决策的影响，并在自主飞行飞机和城市环境中的自动飞行中应用，从而促进科学的进步和前进民族繁荣。自动飙升的飞机为旅行，侦察和观察提供了新的节油方法，包括在大气中难以到达的地区。他们的内置模拟器对大气边界层和上升气流的存在做出了假设，以最佳从盛行的风中提取能量。计算模型还集成在无人机的计划和控制架构中，因为它们根据周围流场的估计来预测通过城市基础设施的最佳路径。在不评估预测的不确定性的情况下，这种模拟器可能会导致次优或灾难性的决定，因为错过了最佳性能或违反安全性限制的机会。该项目通过开发新的，快速和自动化的算法来解决这一挑战，以严格量化不确定性和相应地更新计算模型，并使用在受控但复杂的风条件下使用实验飞机验证这些算法。这项研究与旨在通过围绕相关案例研究建立的一系列研讨会，航空航天工程课程的新数据科学课，以及一系列的研讨会，将有关建模，数据科学和统计学的计算观点融入到工程学的学生和公众。与Ann Arbor动手博物馆建立合作伙伴关系，开发K-8学生及其父母可以访问的展览。这项研究旨在开发自动化方法的基础，以推导特定问题的多余性不确定性量化技术。这些技术旨在将信息从模拟和忠诚度和成本变化的数据源中融合在一起，以比最高保真度模型所要求的计算成本明显低于计算成本，以实现准确的预测。当前的实现是基于启发式方法，这些启发式方法不适合给定问题的数据源之间的特定关系。为了克服这一局限性，该研究将通过分析贝叶斯后期和最大熵分布来得出新的统计估计量，而仅具有可用的信息而不是启发式信息；使用这些估计器开发新的过滤，状态估计和贝叶斯推理技术；并说明如何将这些技术应用于挑战性的非线性，混乱和非高斯动力系统，这些动力系统在计划和控制复杂风场中高涨的飞机的背景下产生。该奖项反映了NSF的法定任务，并被视为值得通过的支持。使用基金会的智力优点和更广泛的影响评估标准进行评估。