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The state reconstruction problem of a heterogeneous dynamic system undersporadic measurements is considered. This system consists of a conversationflow together with a multi-agent network modeling particles within the flow. Wepropose a partial-state reconstruction algorithm using physics-informedlearning based on local measurements obtained from these agents. Trafficdensity reconstruction is used as an example to illustrate the results and itis shown that the approach provides an efficient noise rejection.

Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing