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.

网络物理系统（CPSS）是许多现代工程系统中的核心技术，涵盖了汽车，机器人，医疗设备，建筑物，到电网和高级制造系统。随着这些系统的广泛可用性，机器学习（ML）和人工智能（AI）在许多CPS应用程序中都取得了巨大的成功。但是，他们目前的基本挑战是他们通常需要大数据，可能违反基本的物理原则，导致表现不佳甚至失败，并且不会强大地处理现实生活中的杂乱数据。该项目在网络基础结构（CI）中创建了新的方法，算法和软件，该方法将ML/AI与传统的物理知识整合到所谓的物理知识的机器学习（PIML）模型中，以克服这些挑战。 CI建立在PIML的统一理论基础之上，PIML是一种将异质模型组成复合PIML模型的框架和软件，以及提高其效率和准确性的新方法。开发的技术将推动CPSS中ML/AI的前沿，为克服固有的挑战并增强AI驱动的CPSS的性能和安全性开辟了新的令人兴奋的途径，从而扩大了其现实生活中的应用。该项目将研究活动与教育活动深入融合，以激发本科和研究生水平的计算机科学和工程学的体验式学习和研究经验，并通过与当地学校和公共计划的合作来促进代表性不足的群体中的STEM参与，并丰富公众的理解。该项目通过促进科学进步并促进国家健康，繁荣和福利来为国家利益，如NSF的使命所述，该项目的总体目标是将ML和物理学整合到全面，灵活的和物理学中，并协同综合CI，用于复合PIML和动态系统的积极学习。为此，其目标是开发（1）统一PIML框架的理论基础；（2）一个理论框架和软件，用于在复合PIML模型中构成模型和物理性质；（3）物理信息有效的学习方法，这些方法直接整合物理学，以获取与物理学一致的最有用的数据，以提高样本效率和学习的准确性。这项研究推进了有关PIML方法统一的知识状态，PIML的收益和成本，如何有效，有效地在异质PIML模型中有效地构成模型和物理特性，以及如何将物理特性整合到主动学习中。它还创建了方法和软件，可以使动态系统的新型数据驱动建模方法的快速开发和探索，推动限制并增强CPSS中ML的适用性和性能。通过为整合物理和ML的稳固基础，以产生准确，可解释，健壮和身体一致的模型，CI将促进高性能数据驱动的预测，模拟，仿真，优化和控制方法，从而受益于广泛的科学和工程应用。该奖项反映了NSF的构建范围，并依赖于NSF的Inforthorial Indunitual teem teem teem teem teem eymed eymed，其价值是依据，并且是依据的价值。审查标准。

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