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

这个NSF CPS项目旨在开发用于建模网络物理系统的新技术，以解决现代工程中与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，包括互联能源网络等关键基础设施。这将通过数据驱动技术和基于物理的方法的新颖组合来实现，以提供数学和计算模型，这些模型既足够抽象，可以让人类做出关键的工程决策，又足够精确，可以做出定量预测。该项目的智力价值包括新兴数据科学和模型分析方法的新融合，包括流形学习和信息几何。该项目更广泛的影响包括对大学生的培训，包括来自代表性不足的社区的大学生，一些外联活动，以及公开提供的开源软件。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。该项目将开发克服这一内在矛盾的技术。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泛化能力的黑盒预测模型。与此同时，信息几何学的最新进展产生了模型简化方法，这些方法系统地从物理第一原理中推导出简单的、可解释的模型，这些物理第一原理总结了模型可移植性所需的相关机制。结合这些技术将使“物理上可解释的”简化模型和定量数据之间的有用映射成为可能。这些数据驱动的工具将使“两全其美”-物理上可解释的模型，使定量预测。 我们将结合联合收割机一个有意义的，定性正确，但定量不准确的减少模型与数据驱动的转换。项目团队汇集了物理建模，能源系统和数据驱动学习领域的专业知识。我们将采用这种方法来解决互联能源网络中的关键运营挑战。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。

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.