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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公共法律117-2)。人工智能(AI)已被用于解决各种网络物理系统(CP)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的模型，或者使它们适应相关领域的类似问题。这个项目将研究如何使用现有的科学知识，以物理和其他学科的理论和分析模型的形式，来改进基于数据驱动的人工智能模型。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的知识一致，并生成可用于帮助指导模型走向新领域的合成数据。通过将领域模型与数据驱动的基于人工智能的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，并向决策者保证，基于人工智能的模型在有限的训练数据和动态和不确定的条件下，在一系列环境中是可信和安全的。该项目还将发展教育和招聘能力，以培训来自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的技术，利用科学知识来证明基于人工智能的模型的有效性，以在给定的操作环境中满足所需的性能指标。第一步是设计评估框架，在将预先训练的人工智能模型用于新的操作环境之前，使用分析模型来量化它们所表现出的物理不一致性。物理不一致性较低的模型可以在不做任何更改的情况下使用。物理不一致性较高的模型将要么使用使用从新操作环境获得的少量数据的转移学习式方法进行微调，要么使用基于物理的分析模型进行完全再培训，以生成与新环境相关的合成数据。这些方法将通过与田纳西州交通部合作预测交通流量和容量的案例研究来验证。总而言之，这些研究活动将提供宝贵的工具，从数据和物理属性的角度来理解复杂CP的行为，以及支持田纳西州立大学工程学院课程现代化的项目和软件。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.