Data-Driven Cyberphysical Systems

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Data-Driven Cyberphysical Systems is a collaborative project supported by the Cyberphysical Systems Program of the NSF.

NSF award numbers: 1646522, 1645648, 1646121, 1645832, 1645964

Participants: Ufuk Topcu, Isil Dillig, Constantine Caramanis and Scott Fish (The Univ of Texas at Austin); Sandipan Mishra (Rensselaer Polytechnic Inst); Alberto Sangiovanni-Vincentelli (Univ of California, Berkeley); Mario Sznaier (Northeastern Univ); and Yisong Yue (California Inst of Technology).

Objectives: This project develops the theory, methods and tools necessary to the central question “how can we, in a data-rich world, design and operate cyberphysical systems differently?” The resulting data-driven techniques will transform the design and operation process into one in which data and models---and human designers and operators---continuously and fluently interact. This integrated view promises capabilities beyond its parts. Explicitly integrating data will lead to more efficient decision-making and help reduce the gap from model-based design to system deployment. Furthermore, it will blend design- and run-time tasks, and help develop cyberphysical systems not only for their initial deployment but also for their lifetime.


Target applications: Data-driven cyberphysical systems are ubiquitous in many sectors including manufacturing, automotive, transportation, utilities and health care. While proposed theory, methods and tools will cut across the spectrum of cyberphysical systems, the project focuses on their implications in the emerging application of additive manufacturing. Even though a substantial amount of engineering time is spent, additive manufacturing processes often fail to produce acceptable geometric, material or electro-mechanical properties. Currently, there is no mechanism for predicting and correcting these systematic, repetitive errors nor to adapt the design process to encompass the peculiarities of this manufacturing style. A data-driven cyberphysical systems perspective has the potential to overcome these challenges in additive manufacturing.