CAREER: Composite Physics-Informed Learning of Dynamic Systems

职业：动态系统的复合物理知情学习

基本信息

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

项目摘要

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.

信息物理系统（CPS）是许多现代工程系统的核心技术，涵盖汽车、机器人、医疗设备、建筑、电网和先进制造系统。随着这些系统数据的广泛可用性，机器学习 (ML) 和人工智能 (AI) 在许多 CPS 应用中取得了巨大成功。然而，他们当前面临的根本挑战是，他们经常需要大数据，可能违反基本物理原理，导致性能不佳甚至失败，并且不能稳健地处理来自现实系统的混乱数据。该项目在网络基础设施 (CI) 中创建新的方法、算法和软件，将 ML/AI 与传统物理知识无缝协同地集成到所谓的物理信息机器学习 (PIML) 模型中，从而克服这些挑战。 CI建立在统一的PIML理论基础、将异构模型组合成复合PIML模型的框架和软件以及提高其效率和准确性的新颖方法之上。所开发的技术将推动 CPS 中 ML/AI 的前沿发展，开辟新的令人兴奋的途径，克服固有的挑战，提高人工智能驱动的 CPS 的性能和安全性，从而扩大其现实生活中的应用。该项目将研究活动与教育活动深度结合，激发和培养本科生和研究生在计算机科学与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，促进代表性不足群体的 STEM 参与并丰富公众的理解。正如 NSF 的使命所言，该项目通过促进科学进步、促进国民健康、繁荣和福利来服务于国家利益。该项目的总体目标是将机器学习和物理学整合到一个全面、灵活、用于复合 PIML 的协同 CI 和动态系统的主动学习。为此，其目标是开发（1）统一PIML框架的理论基础； (2) 复合PIML模型的模型构建和物理特性的理论框架和软件； (3)基于物理的主动学习方法，直接整合物理，获取最符合物理的信息数据，提高学习的样本效率和准确性。这项研究推进了有关 PIML 方法的统一、PIML 的好处和成本、如何有效且高效地在异构 PIML 模型中构建模型和物理属性以及如何将物理属性集成到主动学习中的知识状态。它还创建了方法和软件，能够快速开发和探索动态系统的新型数据驱动建模方法，突破极限并增强机器学习在 CPS 中的适用性和性能。通过为集成物理和机器学习奠定坚实的基础，生成准确、可解释、稳健且物理一致的模型，CI 将促进 CPS 的高性能数据驱动预测、模拟、优化和控制方法，使广泛的领域受益。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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)
影响因子：
0
作者：
Truong X. Nghiem;Ján Drgoňa;Colin N. Jones;Zoltán Nagy;Roland Schwan;Biswadip Dey;A. Chakrabarty
通讯作者：
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