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

北卡罗来纳大学夏洛特分校（UNCC）和克莱姆森大学（Clemson University）之间的这个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。尽管深度学习（DL）方法越来越受欢迎，应用也越来越多样化，但它仍然面临着挑战，尤其是在复杂系统建模方面。这些缺陷包括缺乏健壮性、可伸缩性和可组合性。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形优化细胞计算网络（CCN）的数学工具；CCN提出了一种可组合的模块化，可以将大系统划分为具有相应计算单元的小子系统；ii)增强无碳电力分配系统（epds）的运行能力，以提高能源可持续性（同时避免气候灾害）、能源安全和电力基础设施可靠性为目标。此外，该合作项目将为两所大学人工智能、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。克莱姆森实时电力和智能系统实验室最先进的智能电网设备以及UNCC协同人类+人工智能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。将招募代表性不足的少数民族和妇女群体参加这两个机构的研究。因此，该项目有助于建立一支在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方面知识丰富的新型多元化劳动力队伍。我们解决复杂系统建模和优化这一具有挑战性的问题的方法是统计学习理论、图论、控制理论和优化理论的跨学科研究的新颖融合，这将导致新的动态系统建模。该项目提出了一个原则性框架和数学验证，以1)从数据中自动推断图拓扑，2)为基于强化学习（RL）的改进开发多分辨率图评估，3)为持续进化的CCN模型提供新颖稳定的奖励函数设计原则，从而4)优化具有分布式能源的EPDS中的电压分布。总的来说，我们的图形优化CCN模型的原则数学工具将扩大配电系统的理论和应用范围。该奖项反映了美国国家科学基金会的法定使命，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 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.