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

这个由北卡罗来纳大学夏洛特分校(UNCC)和克莱姆森大学(Clemson)合作的项目旨在解决国家面临的重大挑战和需求，即人工智能和清洁电力和能源系统领域的挑战和需求。在应用日益普及和多样化的同时，深度学习方法仍然面临着挑战，特别是在对复杂系统建模方面。这些问题包括缺乏健壮性、可伸缩性和可组合性。这一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化蜂窝计算网络(CCN)的数学工具；CCN提出了可组合的模块化，可以将大系统划分为具有相应计算单元的小子系统，以及ii)增强无碳配电系统(EPDS)的运行，目标是提高能源可持续性(同时避免气候灾难)、能源安全和电力基础设施的可靠性。此外，这个合作项目将为这两个机构的人工智能、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。克莱姆森实时电力和智能系统实验室最先进的智能电网设备以及赔偿委员会协同人类+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面向高中生的外联活动。将招募人数不足的少数群体和妇女群体参加这两个机构的研究。因此，该项目有助于创建一支新的、多样化的劳动力队伍，他们精通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。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中跨学科研究的新融合，这将导致新的动态系统建模。该项目提出了一个原则性的框架和数学验证：1)从数据中自动推断图拓扑，2)基于强化学习(RL)的精化的多分辨率图评估，3)为不断进化的CCN模型提供新颖而稳定的奖励函数设计原则，从而4)优化分布式能源EPDS中的电压分布。总体而言，我们用于图形优化CCN模型的原则性数学工具将拓宽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优势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.