  In this paper, we introduce the concept of observability of targeted state
variables for systems that may not be fully observable. For their estimation,
we introduce and exemplify a deep filter, which is a neural network
specifically designed for the estimation of targeted state variables without
computing the trajectory of the entire system. The observability definition is
quantitative rather than a yes or no answer so that one can compare the level
of observability between different sensor locations.
