Design.R – AI-assisted CPS Design

    Sponsors: DARPA I2O
    PI: Péter Völgyesi
    CoPIs: Christopher White, Janos Sztipanovits, Xenofon Koutsoukos, Akos Ledeczi
    Years active: 2020 - 2024
    Funding: $ 8.7 million
    Staffing: 6 faculty (part-time), 5 research engineer, 3 post docs, 9 grad students
    Partners: University of Alberta, University of Szege

    The project is part of the Symbiotic Design for CPS (SDCPS) program, with a goal to develop AI-based approaches to enable correct-by-construction design of military-relevant CPS. Beyond novel theoretical discoveries we focus our innovation and research efforts to deliver AI-based Co-Designers that are integratable with the dominantly model-based Cyber-Physical System (CPS) design flows and tool suites. Our vision is the reformulation of the conventional engineering process of CPS as a continuously learning, self-improving process of collaborative discovery. Breakthroughs will emerge from the symbiosis of new, AI-based data-driven approaches in design flows to complement human intuitions and classical analytics for synthesizing and validating candidate solutions.

    The project is led by the Institute for Software Integrated Systems of Vanderbilt, and includes collaborators from University of Alberta, Canada and University of Szeged, Hungary. Vanderbilt’s Péter Völgyesi (PI) has over two decades of experience with model-based design, design automation and integration platforms. The Institute for Software Integrated Systems has pioneered generations of metaprogrammable tool suites for modeling and model transformation and their use in design automation. The University of Alberta team, led by Prof. Csaba Szepesvári, has developed several fundamentally novel AI/ML algorithms that led to breakthroughs, such as DeepMind's AlphaGo. As the lead of the foundation group at Google's DeepMind, Prof. Szepesvári has a broad perspective on recent advancements in AI that can change the status quo in model-based design automation. Prof. Miklós Maróti, the lead of the mathematics research team at University of Szeged, has foundational work in applying AI methods within mathematics: augmenting SAT solvers with AI-based approximations to solve algebraic problems and proving stability properties of dynamical systems by learning their Lyapunov functions.

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