Collaborative Research: CPS: Medium: Physics-Model-Based Neural Networks Redesign for CPS Learning and Control

合作研究：CPS：中：基于物理模型的神经网络重新设计用于 CPS 学习和控制

• 批准号: 2311084
• 负责人: Yanbing Mao
• 金额: $ 29.63万
• 依托单位: Wayne State 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: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311084&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Physics

Deep Neural Networks (DNN) enabled Cyber-Physical Systems (CPS) hold great promise for revolutionizing many industries, such as drones and self-driving cars. However, the current generation of DNN cannot provide analyzable behaviors and verifiable properties that are necessary for safety assurance. This critical flaw in purely data-driven DNN sometimes leads to catastrophic consequences, such as vehicle crashes linked to self-driving and driver-assistance technologies. On the other hand, physics-model-based engineering methods provide analyzable behaviors and verifiable properties, but do not match the performance of DNN systems. These considerations motivate the work in this project which aims at physics-model-based neural networks (NN) redesign, yielding HyPhy-DNN: a hybrid self-correcting physics-enhanced DNN framework. HyPhy-DNN will provide the performance of DNNs and the analyzability and verifiability of physical models, thus providing a foundation for verifiably safe self-driving cars, drones, and other CPS systems. Moreover, the HyPhy-DNN will fundamentally advance the integration of deep learning and robust control to enable safety- and time-critical CPS to safely operate with high performance in unforeseen and dynamic environments.The HyPhy-DNN will make three innovations in redesigning NN architecture: (i) Physics augmentations of NN inputs for directly capturing hard-to-learn physical quantities and embedding Taylor series; (ii) Physics-guided neural network editing, such as removing links between independent physics variables or fixed weights on links between certain physics variables to maintain the known physics identity such as in conservation laws; and (iii) Time-frequency-representation filtering-based activations for filtering out noise having dynamic frequency distribution. The novel architectural redesigns will empower the HyPhy-DNN with four targeted capabilities: 1) controllable and provable model accuracy; 2) maximum avoidance of spurious correlations; 3) strict compliance with physics knowledge; and 4) automatic correction of unsafe control commands. Finally, the safety certification of any DNN will be a long-term challenge. Hence, the HyPhy-DNN shall have a simple but verified backup controller for guaranteeing safe and stable operation in dynamic and unforeseen environments. To achieve this, the research team will integrate HyPhy-DNN with an adaptive-model-adaptive-control (AMAC) framework, the core novelty of which lies in fast and accurate nonlinear model learning via sparse regression for model-based robust control. The HyPhy-DNN and AMAC are complementary and will be interactive at different scales of system performance and functionalities during the safety-status-cycle, supported by the Simplex software architecture, a well-known real-time software technology that tolerates faults and allows online control system upgrades.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.

深度神经网络（DNN）启用了网络物理系统（CPS），可以彻底改变许多行业，例如无人机和自动驾驶汽车。但是，当前的DNN无法提供可分析的行为和可验证的安全性，这些属性是安全保证所必需的。纯粹由数据驱动的DNN中的这种关键缺陷有时会导致灾难性后果，例如与自动驾驶和驾驶员辅助技术有关的车辆崩溃。另一方面，基于物理模型的工程方法提供了可分析的行为和可验证的属性，但不符合DNN系统的性能。这些考虑因素激发了该项目的工作，该项目旨在重新设计基于物理模型的神经网络（NN），产生Hyphy-DNN：一种混合自我校正物理学增强的DNN框架。 Hyphy-DNN将提供DNN的性能以及物理模型的分析性和可验证性，从而为可验证安全的自动驾驶汽车，无人机和其他CPS系统提供基础。 此外，Hyphy-DNN从根本上可以提高深度学习和健壮控制的整合，以使安全性和关键时期的CP能够在不可预见和动态的环境中安全地使用高性能。Hyphy-DNN将对NN结构进行三项创新，以重新设计NN结构：（I）启动型号的量级范围，以直接捕获NN NN的量化量，并促成了nn NN的范围，并将其直接限制为裸露的型号。 （ii）物理引导的神经网络编辑，例如在某些物理变量之间删除独立物理变量之间的联系或固定权重，以维持已知的物理身份，例如在保护法中； （iii）基于动态频率分布的噪声过滤的基于时频陈述过滤的激活。新型的体系结构重新设计将赋予Hyphy-DNN的能力四个有针对性的功能：1）可控且可证明的模型精度； 2）最大避免伪造相关性； 3）严格遵守物理知识； 4）自动校正不安全的控制命令。最后，任何DNN的安全认证都是长期挑战。因此，Hyphy-DNN应具有一个简单但经过验证的备份控制器，以确保在动态和不可预见的环境中进行安全稳定的操作。为了实现这一目标，研究团队将将Hyphy-DNN与自适应模型自适应控制（AMAC）框架整合在一起，该框架的核心新颖性在于，通过基于模型的强大控制，其快速准确的非线性模型学习。 Hyphy-DNN和AMAC是互补的，在安全状态循环期间的系统性能和功能的不同范围内将具有互动性，在单纯型软件体系结构的支持下，这是一种众所周知的实时软件技术，可容忍故障并允许在线控制系统升级。这一奖项反映了NSF的法定任务，并反映了审查的范围，并通过评估了构成的范围。

项目成果

Phy-Taylor: Partially Physics-Knowledge-Enhanced Deep Neural Networks via NN Editing

Phy-Taylor：通过 NN 编辑部分物理知识增强的深度神经网络

• DOI: 10.1109/tnnls.2023.3325432
• 发表时间: 2024
• 期刊: IEEE Transactions on Neural Networks and Learning Systems
• 影响因子: 10.4
• 作者: Mao, Yanbing;Gu, Yuliang;Sha, Lui;Shao, Huajie;Wang, Qixin;Abdelzaher, Tarek
• 通讯作者: Abdelzaher, Tarek