AI Institute in Dynamic Systems

NSF人工智能（AI）动态系统研究所将改变基础人工智能（AI）和机器学习（ML）理论、算法和应用的研究和教育。该研究所的具体目标是为复杂动态系统（如自动驾驶汽车、机器人、电网、流体流动控制、数字孪生和/或先进制造）提供安全、有保证的实时学习和控制。AI/ML方法是一套数据科学算法，可以利用所有科学和工程学科的各种传感器数据流。这使得动态系统的传统建模和计算能够与新兴的AI/ML算法方法集成，这些系统随时间发展并受物理控制。因此，这为实时传感、学习、预测和决策挑战提供了安全、可靠、高效和道德的数据支持解决方案。此外，该研究所还将传播开源软件和教育材料，以促进本科生、研究生和工程专业人员的专业发展。该研究所将进一步开发一个社区范围的通用任务框架，用于评估物理和工程领域挑战数据集上ML/AI算法的分类，从而为工程AI/ML社区提供广泛的服务。动态系统人工智能研究所将通过发展四个关键学科的数学基础来改变基于物理的人工智能/机器学习算法的基础：(i)控制理论，（ii）概率和统计，（iii）优化，（iv）动力系统（建模）。这四个学科的整合对于AI/ML算法的开发至关重要，这些算法可以被工程系统所利用。在这些学科之间建立严格的数学联系是我们在工程动态系统中重新构建AI/ML基础的努力的驱动灵感。这些基础性工作将产生以下项目重点：(i)人工智能的数学基础，（ii）人工智能的重大挑战应用，以及（iii）人工智能工程的转型教育和劳动力发展基础设施。基于物理的机器学习正在成为汇集各种AI/ML算法和动态系统工程的领先范例，为实时传感、学习、决策和预测提供新的功能，这些功能安全、可靠、高效、合乎道德，并充满了不确定性量化（UQ）。我们的团队将专注于开发一个通用的、灵活的AI/ML框架，以快速学习新的物理，执行已知的物理约束，并直接发现它们。我们将通过在现实世界的大挑战应用中进行演示，不断评估我们的方法，以便这些方法具有物理动机，并在多个领域催化进步。这种对共同任务框架的评估将产生AI/ML算法的广泛和原则性分类。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 NSF Artificial Intelligence (AI) Research Institute in Dynamic Systems will transform research and education in fundamental artificial intelligence (AI) and machine learning (ML) theory, algorithms, and applications. The institute’s specific aim is to empower safe, guaranteed real-time learning and control of complex dynamic systems, such as autonomous vehicles, robotics, power grids, fluid flow control, digital twins, and/or advanced manufacturing. AI/ML methods are a suite of data science algorithms that leverage diverse sensor data streams across all disciplines of science and engineering. This enables the integration of traditional modeling and computation of dynamic systems, which evolve in time and are governed by physics, with emerging AI/ML algorithmic approaches. As a result, this allows for safe, reliable, efficient, and ethical data-enabled solutions to real-time sensing, learning, prediction, and decision-making challenges. In addition, the institute will disseminate open-source software and educational materials for the professional development of undergraduate students, graduate students, and engineering professionals alike. The institute will further develop a community-wide common task framework for evaluating a taxonomy of ML/AI algorithms on challenge data sets in physics and engineering, thus providing a broad service to the engineering AI/ML community. The AI Institute in Dynamic Systems will transform the foundations of physics-informed AI/ML algorithms by developing the mathematical foundations in four key disciplines: (i) control theory, (ii) probability and statistics, (iii) optimization, and (iv) dynamical systems (modeling). The integration of all four of these disciplines is critical for the development of AI/ML algorithms that can be leveraged by engineered systems. Establishing rigorous mathematical connections between these disciplines is a driving inspiration for our efforts in re-framing the foundations of AI/ML for the dynamic systems in engineering. Such foundational efforts will engender the following program thrusts: (i) the mathematical foundations of AI, (ii) grand challenge applications for AI, and (iii) a transformational educational and workforce development infrastructure for AI Engineering. Physics-informed ML is emerging as a leading paradigm for bringing together a diverse suite of AI/ML algorithms and dynamic systems engineering, providing new capabilities in real-time sensing, learning, decision making, and predictions that are safe, reliable, efficient, ethical, and imbued with uncertainty quantification (UQ). Our team will focus on developing a general and flexible AI/ML framework to rapidly learn new physics, enforce known physical constraints, and discover them directly. We will continuously evaluate our methods through demonstrations on real-world grand challenge applications so that the methods are physically motivated, and advances are catalyzed across multiple domains. Such an evaluation on a common task framework will engender a broad and principled taxonomy of AI/ML algorithms.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.