  Deep neural state-space models (SSMs) provide a powerful tool for modeling
dynamical systems solely using operational data. Typically, neural SSMs are
trained using data collected from the actual system under consideration,
despite the likely existence of operational data from similar systems which
have previously been deployed in the field. In this paper, we propose the use
of model-agnostic meta-learning (MAML) for constructing deep encoder
network-based SSMs, by leveraging a combination of archived data from similar
systems (used to meta-train offline) and limited data from the actual system
(used for rapid online adaptation). We demonstrate using a numerical example
that meta-learning can result in more accurate neural SSM models than
supervised- or transfer-learning, despite few adaptation steps and limited
online data. Additionally, we show that by carefully partitioning and adapting
the encoder layers while fixing the state-transition operator, we can achieve
comparable performance to MAML while reducing online adaptation complexity.
