Collaborative Research: MoDL: Graph-Optimized Cellular Connectionism via Artificial Neural Networks for Data-Driven Modeling and Optimization of Complex Systems

合作研究：MoDL：通过人工神经网络进行图优化的细胞连接，用于复杂系统的数据驱动建模和优化

• 批准号: 2234031
• 负责人: Minwoo Lee
• 金额: $ 22.41万
• 依托单位: University of North Carolina at Charlotte
• 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=2234031&HistoricalAwards=false
• 关键词: Collaborative; Research; MoDL; Graph; Optimized

This collaborative project between University of North Carolina at Charlotte (UNCC) and Clemson University (Clemson) aims at addressing significant national challenges and needs, namely in the fields of artificial intelligence and clean electric power and energy systems. While growing in popularity and diversity of applications, deep learning (DL) methods nonetheless confront challenges especially for modeling complex systems. These include lack of robustness, scalability, and composability. The research outcomes of this collaborative project will be: i) mathematical tools for understanding and designing a graph-optimized Cellular Computational Network (CCN) for complex system modeling and optimization; CCN suggests a composable modularity that can divide a large system into small subsystems with corresponding computational cells and ii) empowering the operation of carbon-free electric power distribution systems (EPDSs), with goals of improving energy sustainability (while avoiding climate disasters), energy security, and electricity infrastructure reliability. Furthermore, this collaborative project will provide unique research training to graduate and undergraduate students in the disciplines of artificial intelligence, machine learning, and power systems engineering at the two institutions. The state-of-the-art smart grid equipment at Real-Time Power and Intelligent Systems Lab at Clemson and high-performance computing systems and AI equipment at Synergistic Human+AI Research lab at UNCC will be used to impact outreach activities to high school students. Underrepresented minority and women groups will be recruited to participate in the research at the two institutions. Therefore, this project contributes to the creation of a new, diverse workforce knowledgeable in machine learning and AI, smart grid/power system technologies, and renewable energy. Our approach to address the challenging problem of complex system modeling and optimization constitute a novel blend of interdisciplinary study in statistical learning theory, graph theory, control theory, and optimization theory that will lead to novel dynamic system modeling. The project proposes a principled framework and mathematical validation to 1) automatically infer a graph topology from data, 2) develop multi-resolution graph evaluation for reinforcement learning (RL)-based refinement, 3) provide novel and stable reward function design principle for a continuously evolving CCN model, and thus 4) optimize the voltage profile in an EPDS with distributed energy resources. Overall, our principled mathematical tools for graph-optimized CCN models will broaden the scope of theory and applications in an electric power distribution 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.

北卡罗来纳大学夏洛特分校 (UNCC) 和克莱姆森大学 (Clemson) 之间的这一合作项目旨在解决重大的国家挑战和需求，即人工智能和清洁电力和能源系统领域。尽管应用日益普及和多样化，深度学习 (DL) 方法仍然面临着挑战，尤其是在复杂系统建模方面。其中包括缺乏稳健性、可扩展性和可组合性。该合作项目的研究成果将是：i）用于理解和设计用于复杂系统建模和优化的图优化蜂窝计算网络（CCN）的数学工具； CCN 提出了一种可组合的模块化，可以将大型系统划分为具有相应计算单元的小型子系统，ii) 增强无碳配电系统 (EPDS) 的运行，目标是提高能源可持续性（同时避免气候灾害）、能源安全和电力基础设施可靠性。此外，该合作项目将为两所机构的人工智能、机器学习和电力系统工程学科的研究生和本科生提供独特的研究培训。克莱姆森大学实时电力和智能系统实验室的最先进智能电网设备以及北卡罗来纳大学人类+人工智能协同研究实验室的高性能计算系统和人工智能设备将用于影响高中生的外展活动。代表性不足的少数族裔和妇女群体将被招募参与这两个机构的研究。因此，该项目有助于打造一支熟悉机器学习和人工智能、智能电网/电力系统技术和可再生能源的新型、多元化的劳动力队伍。 我们解决复杂系统建模和优化这一挑战性问题的方法构成了统计学习理论、图论、控制理论和优化理论等跨学科研究的新颖融合，这将带来新颖的动态系统建模。该项目提出了一个原理框架和数学验证，以实现 1) 从数据中自动推断图形拓扑，2) 开发用于基于强化学习 (RL) 的细化的多分辨率图形评估，3) 为不断发展的 CCN 模型提供新颖且稳定的奖励函数设计原理，从而 4) 优化具有分布式能源的 EPDS 中的电压分布。总体而言，我们用于图优化 CCN 模型的原则性数学工具将扩大配电系统的理论和应用范围。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。