Collaborative Research: CISE-MSI: RCBP-RF: IIS-RI: Analytically-based frameworks for AI model verification and improvement in cyber-physical systems

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。人工智能（AI）已被用于解决各种网络物理系统（CPS）中的许多复杂工程问题，如自动驾驶、智能制造、高效生产系统和系统缺陷的快速诊断。然而，通常很难理解这些人工智能技术从数据中学习到的模型，或者将它们应用于相关领域的类似问题。该项目将研究现有的科学知识，以物理和其他学科的理论和分析模型的形式，如何用于改进数据驱动的基于人工智能的模型。现有的科学知识可以用来检查数据驱动的模型是否与现有知识一致，并生成可用于帮助将模型引导到新领域的综合数据。通过将领域模型与基于数据驱动的人工智能模型进行更深层次的耦合，该项目将为开发安全的CPS提供新的工具，并为决策者提供保证，即基于人工智能的模型在有限的训练数据和动态和不确定条件下的一系列环境中是值得信赖和安全的。该项目还将发展教育和招聘能力，以培训历史上在工程领域代表性不足的群体的工程师。该项目旨在开发一套新技术，利用科学知识来证明基于人工智能的模型的有效性，以满足给定操作环境中所需的性能指标。第一步是设计评估框架，使用分析模型来量化预训练的人工智能模型在新的操作环境中使用之前所表现出的物理不一致性。具有低物理不一致性的模型无需任何更改即可使用。具有较高物理不一致性的模型将使用迁移学习风格的方法进行微调，这种方法使用从新的操作环境中获得的少量数据，或者使用基于物理的分析模型进行完全重新训练，以生成与新环境相关的合成数据。这些方法将与田纳西州运输部合作，通过预测交通流量和容量的案例研究进行验证。总之，这些研究活动将提供有价值的工具，从数据和物理属性的角度来理解复杂CPS的行为，以及项目和软件，以支持田纳西州立大学工程学院的课程现代化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Artificial intelligence (AI) has been used to solve many complex engineering problems across diverse cyber-physical systems (CPS) such as autonomous driving, smart manufacturing, efficient production systems, and rapid diagnosis of system defects. However, it is often difficult to understand the models learned by these AI techniques from data, or to adapt them to similar problems in related domains. This project will study how existing scientific knowledge, in the form of theoretical and analytical models from physics and other disciplines, can be used to improve data driven AI-based models. Existing scientific knowledge could be used to sanity check that data-driven models agree with existing knowledge and to generate synthetic data that can be used to help guide the models toward new domains. Through deeper coupling of domain models with data-driven AI-based models, the project will provide new tools for developing safe CPS and give decision-makers assurance that an AI-based model is trustworthy and safe to operate in a range of environments under limited training data and dynamic and uncertain conditions. The project will also develop educational and recruiting capacity to train engineers from groups historically underrepresented in engineering.This project aims to develop a set of novel techniques that use scientific knowledge to certify the effectiveness of AI-based models to meet desired performance metrics in a given operational environment. The first step is to design evaluation frameworks that use analytical models to quantify physical inconsistency exhibited by the pre-trained AI models prior to using them in new operational environments. Models with low physical inconsistency can be used without any change. Models with higher physical inconsistency will either be fine-tuned with transfer learning-style approaches that use small amounts of data obtained from the new operational environment, or entirely retrained using the physics-based analytical models to generate synthetic data relevant to the new environment. These methods will be validated using a case study of predicting traffic flows and capacity in partnership with the Tennessee Department of Transportation. Together, the research activities will provide valuable tools to understand the behavior of complex CPS from the point of view of both data and physical properties, as well as projects and software to support curriculum modernization at Tennessee State University College of Engineering.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.