  Accurate structural response prediction forms a main driver for structural
health monitoring and control applications. This often requires the proposed
model to adequately capture the underlying dynamics of complex structural
systems. In this work, we utilize a learnable Extended Kalman Filter (EKF),
named the Neural Extended Kalman Filter (Neural EKF) throughout this paper, for
learning the latent evolution dynamics of complex physical systems. The Neural
EKF is a generalized version of the conventional EKF, where the modeling of
process dynamics and sensory observations can be parameterized by neural
networks, therefore learned by end-to-end training. The method is implemented
under the variational inference framework with the EKF conducting inference
from sensing measurements. Typically, conventional variational inference models
are parameterized by neural networks independent of the latent dynamics models.
This characteristic makes the inference and reconstruction accuracy weakly
based on the dynamics models and renders the associated training inadequate. In
this work, we show that the structure imposed by the Neural EKF is beneficial
to the learning process. We demonstrate the efficacy of the framework on both
simulated and real-world structural monitoring datasets, with the results
indicating significant predictive capabilities of the proposed scheme.

Machine Learning;Computational Engineering, Finance, and Science;Chaotic Dynamics;Machine Learning