Data-Driven Compositional Construction of Symbolic Models for Large-Scale Networks with Unknown Dynamics and Interconnection Topology

This paper proposes a novel data-driven approach to construct symbolic models for large-scale interconnected networks with unknown dynamics and interconnection topologies, enabling formal control synthesis with correctness guarantees, without relying on traditional small-gain conditions.