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.

NSF CPS 项目旨在开发用于建模网络物理系统的新技术，以解决与现代工程的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交互，包括互连等关键基础设施。这将通过数据驱动技术和基于物理的方法的新颖结合来实现，提供数学和计算模型，这些模型既足够抽象，足以被人类做出关键的工程决策所理解，又足够精确，足以做出定量预测。该项目的智力优点包括新颖的融合新兴数据科学和模型分析方法，包括多种学习和信息几何，该项目的更广泛影响包括对本科生（包括来自代表性不足的社区的本科生）的培训、一些外展活动以及公开可用的开源软件。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，但粗略的模型更容易解释。一方面，数据科学和机器学习技术使我们能够有效地构建黑盒。有限的预测模型与此同时，信息几何的最新进展产生了模型简化方法，这些方法从总结模型可转移性所需的相关机制的物理第一原理中系统地导出简单的、可解释的模型，结合这些技术将能够在“物理上可解释的”简化之间建立有用的映射。这些数据驱动的工具将实现“两全其美”——可以进行定量预测的物理可解释模型，我们将建立一个有意义的、定性正确但定量不准确的简化模型与数据驱动的转换。团队聚集在一起我们将应用这种方法来解决互联能源网络中的关键运营挑战，该技术将应用于任何复杂的网络物理系统的建模。该奖项反映了 NSF 的成就。法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。