  The state reconstruction problem of a heterogeneous dynamic system under
sporadic measurements is considered. This system consists of a conversation
flow together with a multi-agent network modeling particles within the flow. We
propose a partial-state reconstruction algorithm using physics-informed
learning based on local measurements obtained from these agents. Traffic
density reconstruction is used as an example to illustrate the results and it
is shown that the approach provides an efficient noise rejection.
