CAREER: Composite Physics-Informed Learning of Dynamic Systems

职业：动态系统的复合物理知情学习

• 批准号: 2238296
• 负责人: Truong Nghiem
• 金额: $ 49.25万
• 依托单位: Northern Arizona University
• 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: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238296&HistoricalAwards=false
• 关键词: CAREER; Composite; Physics; Informed; Learning

Cyber-physical systems (CPSs) are core technologies in many modern engineering systems, spanning from automobiles, robots, medical devices, buildings, to power grids and advanced manufacturing systems. With the wide availability of data from these systems, machine learning (ML) and artificial intelligence (AI) have found great success in many CPS applications. However, their current fundamental challenges are that they often require big data, may violate basic physical principles leading to underperformance or even failures, and do not robustly handle messy data from real-life systems. This project creates new methods, algorithms, and software in a cyberinfrastructure (CI) that seamlessly and synergistically integrate ML/AI with traditional physical knowledge in so-called physics-informed machine learning (PIML) models that can overcome these challenges. The CI is built upon a unified theoretical foundation of PIML, a framework and software for composing heterogeneous models into composite PIML models, and novel methods for improving their efficiency and accuracy. The developed technologies will push forward the frontiers of ML/AI in CPSs to open up new exciting pathways for overcoming the inherent challenges and enhancing the performance and safety of AI-driven CPSs, thus broadening their real-life applications. This project deeply integrates research activities with education activities to excite and foster experiential learning and research experience in computer science and engineering at the undergraduate and graduate levels, and to promote STEM participation among underrepresented groups and enrich public understanding through collaboration with local schools and public programs. The project serves the national interest, as stated by NSF's mission, by promoting the progress of science, and to advance the national health, prosperity, and welfare.The overarching goal of this project is to integrate ML and physics within a comprehensive, flexible, and synergistic CI for composite PIML and active learning of dynamic systems. To this end, its objectives are to develop (1) a theoretical foundation of unified PIML frameworks; (2) a theoretical framework and software for composing models and physical properties in composite PIML models; and (3) physics-informed active learning methods which directly integrate physics to obtain the most informative data consistent with physics for improving the sample efficiency and accuracy of learning. This research advances the state of knowledge regarding unification of PIML methods, the benefits and costs of PIML, how to effectively and efficiently compose models and physical properties in a heterogeneous PIML model, and how to integrate physical properties into active learning. It also creates methodologies and software that enable rapid development and exploration of novel data-driven modeling methods for dynamic systems, pushing the limits and enhancing the applicability and performance of ML in CPSs. By building a solid foundation for integrating physics and ML to yield accurate, interpretable, robust, and physically consistent models, the CI will facilitate high-performance data-driven prediction, simulation, optimization, and control methods for CPSs, benefiting a broad range of scientific and engineering applications.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.

计算机物理系统(CPSS)是许多现代工程系统的核心技术，从汽车、机器人、医疗设备、建筑到电网和先进制造系统。随着这些系统数据的广泛获取，机器学习(ML)和人工智能(AI)在许多CPS应用中都取得了巨大的成功。然而，他们目前面临的根本挑战是，他们经常需要大数据，可能会违反基本的物理原则，导致性能不佳甚至故障，并且不能稳健地处理来自现实系统的杂乱数据。该项目在网络基础设施(CI)中创造了新的方法、算法和软件，在所谓的物理信息机器学习(PIML)模型中将ML/AI与传统物理知识无缝协同地集成在一起，可以克服这些挑战。CI建立在PIML的统一理论基础之上，PIML是一个将异质模型组合成复合PIML模型的框架和软件，以及提高其效率和精度的新方法。所开发的技术将推动ML/AI在CPSS中的前沿，为克服人工智能驱动的CPSS的内在挑战、增强其性能和安全性开辟新的令人兴奋的途径，从而拓宽其现实应用。该项目将研究活动与教育活动深度结合，以激发和培养本科生和研究生在计算机科学和工程方面的体验式学习和研究经验，并通过与当地学校和公共项目的合作，促进未被充分代表的群体参与STEM，并增进公众了解。正如NSF的使命所述，该项目服务于国家利益，通过促进科学进步，促进国家健康、繁荣和福祉。该项目的总体目标是将ML和物理学整合在一个全面、灵活和协同的CI中，用于合成PIML和动态系统的主动学习。为此，其目标是发展(1)统一的PIML框架的理论基础；(2)用于组合PIML模型和物理特性的理论框架和软件；(3)直接结合物理以获得与物理一致的最丰富的信息的主动学习方法，以提高样本学习的效率和准确性。本研究就PIML方法的统一性、PIML的好处和成本、如何在异质PIML模型中有效和高效地组合模型和物理属性以及如何将物理属性整合到主动学习中的知识状况进行了研究。它还创建了方法和软件，使动态系统的新型数据驱动建模方法能够快速开发和探索，推动了ML在CPSS中的极限，并增强了ML的适用性和性能。通过为整合物理学和ML建立坚实的基础，以产生准确、可解释、稳健和物理上一致的模型，CI将促进CPSS的高性能数据驱动的预测、模拟、优化和控制方法，使广泛的科学和工程应用受益。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。

项目成果

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

• DOI: 10.23919/acc55779.2023.10155901
• 发表时间: 2023-05
• 期刊: 2023 American Control Conference (ACC)
• 作者: Truong X. Nghiem;Ján Drgoňa;Colin N. Jones;Zoltán Nagy;Roland Schwan;Biswadip Dey;A. Chakrabarty

Causal Deep Operator Networks for Data-Driven Modeling of Dynamical Systems

用于动力系统数据驱动建模的因果深度算子网络

• DOI: 10.1109/smc53992.2023.10394294
• 发表时间: 2023
• 期刊: and Cybernetics
• 作者: Nghiem, Truong X.;Nguyen, Thang;Nguyen, Binh T.;Nguyen, Linh
• 通讯作者: Nguyen, Linh

Connectivity-Preserving Distributed Informative Path Planning for Mobile Robot Networks

• DOI: 10.1109/lra.2024.3362133
• 发表时间: 2024-03
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Binh T. Nguyen;Truong X. Nghiem;Linh Nguyen;H. M. La;Thang Nguyen