CAREER: Composite Physics-Informed Learning of Dynamic Systems

About This Grant

Cyber-physical systems (CPSs) are core technologies in many modern engineering systems, spanning from automobiles, robots, medical devices, buildings, to power grids and advanced manufacturing systems. With the wide availability of data from these systems, machine learning (ML) and artificial intelligence (AI) have found great success in many CPS applications. However, their current fundamental challenges are that they often require big data, may violate basic physical principles leading to underperformance or even failures, and do not robustly handle messy data from real-life systems. This project creates new methods, algorithms, and software in a cyberinfrastructure (CI) that seamlessly and synergistically integrate ML/AI with traditional physical knowledge in so-called physics-informed machine learning (PIML) models that can overcome these challenges. The CI is built upon a unified theoretical foundation of PIML, a framework and software for composing heterogeneous models into composite PIML models, and novel methods for improving their efficiency and accuracy. The developed technologies will push forward the frontiers of ML/AI in CPSs to open up new exciting pathways for overcoming the inherent challenges and enhancing the performance and safety of AI-driven CPSs, thus broadening their real-life applications. This project deeply integrates research activities with education activities to excite and foster experiential learning and research experience in computer science and engineering at the undergraduate and graduate levels, and to promote STEM participation among underrepresented groups and enrich public understanding through collaboration with local schools and public programs. The project serves the national interest, as stated by NSF's mission, by promoting the progress of science, and to advance the national health, prosperity, and welfare. The overarching goal of this project is to integrate ML and physics within a comprehensive, flexible, and synergistic CI for composite PIML and active learning of dynamic systems. To this end, its objectives are to develop (1) a theoretical foundation of unified PIML frameworks; (2) a theoretical framework and software for composing models and physical properties in composite PIML models; and (3) physics-informed active learning methods which directly integrate physics to obtain the most informative data consistent with physics for improving the sample efficiency and accuracy of learning. This research advances the state of knowledge regarding unification of PIML methods, the benefits and costs of PIML, how to effectively and efficiently compose models and physical properties in a heterogeneous PIML model, and how to integrate physical properties into active learning. It also creates methodologies and software that enable rapid development and exploration of novel data-driven modeling methods for dynamic systems, pushing the limits and enhancing the applicability and performance of ML in CPSs. By building a solid foundation for integrating physics and ML to yield accurate, interpretable, robust, and physically consistent models, the CI will facilitate high-performance data-driven prediction, simulation, optimization, and control methods for CPSs, benefiting a broad range of scientific and engineering applications. 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.