Collaborative Research: CPS: Medium: Data Driven Modeling and Analysis of Energy Conversion Systems -- Manifold Learning and Approximation

合作研究：CPS：媒介：能量转换系统的数据驱动建模和分析——流形学习和逼近

• 批准号: 2223987
• 负责人: Yannis Kevrekidis
• 金额: $ 40万
• 依托单位: Johns Hopkins 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: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223987&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Data

This NSF CPS project aims to develop new techniques for modeling cyber-physical systems that will address fundamental challenges associated with scale and complexity in modern engineering. The project will transform human interaction with complex cyber-physical and engineered systems, including critical infrastructure such as interconnected energy networks. This will be achieved through a novel combination of data-driven techniques and physics-based approaches to give mathematical and computational models that are at once abstract enough to be understood by humans making key engineering decisions and precise enough to make quantitative predictions. The intellectual merits of the project include a novel confluence of emerging data science and model-analysis methods, including manifold learning and information geometry. The broader impacts of the project include the training of undergraduates, including those from underrepresented communities, several outreach activities, and publicly available open-source software.Engineering requirements often make incompatible demands on models. Detailed models make highly accurate predictions, but coarse models are easier to interpret. This project will develop techniques to overcome this inherent contradiction. On the one hand, data science and machine learning techniques allow us to efficiently construct black box predictive models with limited generalizability. At the same time, recent advances in information geometry have produced model reduction methods that systematically derive simple, interpretable models from physical first principles that summarize relevant mechanisms needed for model transferability. Combining these technologies will enable useful mappings between “physically explainable” reduced models and quantitative data. These data-driven tools will enable “the best of both worlds” – physically interpretable models that make quantitative predictions. We will combine a meaningful, qualitatively correct but quantitatively inaccurate reduced model with a data-driven transformation. The project team brings together domain-specific expertise in physical modeling, energy systems, and data-driven learning. We will apply this approach to address key operational challenges in interconnected energy networks. The enabling technology will apply to modeling any complex cyber-physical system.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NSF CPS项目旨在开发用于建模网络物理系统的新技术，这些技术将解决与现代工程中规模和复杂性相关的基本挑战。该项目将改变人类与复杂的网络物理和工程系统的互动，包括关键基础设施，例如互连能量网络。这将通过数据驱动的技术和基于物理的方法的新颖组合来实现，以提供数学和计算模型，这些模型立即具有足够的抽象，以至于人类做出关键的工程决策并精确地进行了足够精确的预测以做出定量预测。该项目的智力优点包括新兴数据科学和模型分析方法的新颖融合，包括流形学习和信息几何形状。该项目的更广泛影响包括对本科生的培训，包括代表性不足的社区，几个外展活动以及公开可用的开源软件的培训。工程要求通常会对模型提出不兼容的要求。详细的模型可以做出高度准确的预测，但是粗糙的模型易于解释。该项目将开发技术来克服这种继承矛盾。一方面，数据科学和机器学习技术使我们能够有效地构建具有有限通用性的黑匣子预测模型。同时，信息几何形状的最新进展产生了模型还原方法，这些方法从物理第一原理中系统地得出了简单，可解释的模型，这些模型汇总了模型可传递性所需的相关机制。结合这些技术将使“物理解释”减少模型和定量数据之间有用。这些数据驱动的工具将实现“两全其美”的“两全其美” - 可以进行定量预测的物理解释模型。我们将结合一个有意义的，定性正确但定量不正确的减少模型，并通过数据驱动的转换。项目团队汇集了特定领域的物理建模，能源系统和数据驱动学习方面的专业知识。我们将采用这种方法来解决互连能源网络中的关键运营挑战。这项有利的技术将适用于对任何复杂的网络物理系统进行建模。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的审查标准，认为值得通过评估值得支持。