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，高效生产系统和快速诊断系统缺陷。然而，通常很难理解这些人工智能技术从数据中学习的模型，或者使它们适应相关领域的类似问题。该项目将研究现有的科学知识，以物理学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AI的模型。现有的科学知识可以用于检查数据驱动模型是否与现有知识一致，并生成合成数据，用于帮助引导模型进入新的领域。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入的耦合，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以及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。该项目还将开发教育和招聘能力，以培训工程师，这些工程师来自历史上在工程领域代表性不足的群体。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AI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可以在没有任何改变的情况下使用。具有较高物理不一致性的模型将使用迁移学习式方法进行微调，该方法使用从新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。这些方法将使用预测交通流量和容量的案例研究与田纳西州交通部合作进行验证。总之，研究活动将提供有价值的工具，从数据和物理特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。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.