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大学（UNCC）和克莱姆森大学（Clemson）之间的合作项目旨在解决重大的国家挑战和需求，即在人工智能和清洁电力和能源系统领域。尽管在应用程序的流行和多样性中，深度学习（DL）方法仍然面临着挑战，尤其是在建模复杂系统时面临挑战。这些包括缺乏鲁棒性，可伸缩性和合并性。该协作项目的研究结果将是：i）用于理解和设计用于复杂系统建模和优化的图形优化的蜂窝计算网络（CCN）的数学工具； CCN提出了一个可组合的模块化，可以将大型系统划分为具有相应计算单元的小子系统，ii）赋予无碳电力发电系统（EPDSS）的运行能力，并具有改善能源可持续性的目标（避免气候灾难），能源安全和电力基础设施的可靠性。此外，这个协作项目将在两个机构的人工智能，机器学习和电力系统工程学科中为研究生和本科生提供独特的研究培训。 Clemson的实时功率和智能系统实验室的最先进的智能电网设备以及UNCC协同人类+AI研究实验室的高性能计算系统和AI设备将用于影响高中生的外展活动。将招募代表性不足的少数民族和妇女团体参加这两个机构的研究。因此，该项目有助于创建一个新的，多样化的劳动力在机器学习和AI，智能电网/电力系统技术和可再生能源方面的知识。 我们解决复杂系统建模和优化的具有挑战性问题的方法构成了统计学习理论，图理论，控制理论和优化理论中跨学科研究的新型融合，该理论将导致新型的动态系统建模。该项目提出了一个原则性的框架和数学验证1）从数据中自动推断出图形拓扑，2）开发基于加固学习的多分辨率图评估（RL）改进，3）为连续发展的CCN模型提供新颖且稳定的奖励功能设计原​​理，从而优化与分布分布的Epdage Epdage Epdage progitige。总体而言，我们针对图形优化的CCN模型的原则数学工具将扩大电力发电系统中理论和应用的范围。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响标准通过评估来通过评估来支持的。