  In general, many dynamic processes are involved with interacting variables,
from physical systems to sociological analysis. The interplay of components in
the system can give rise to confounding dynamic behavior. Many approaches model
temporal sequences holistically ignoring the internal interaction which are
impotent in capturing the protogenic actuation. Differently, our goal is to
represent a system with a part-whole hierarchy and discover the implied
dependencies among intra-system variables: inferring the interactions that
possess causal effects on the sub-system behavior with REcurrent partItioned
Network (REIN). The proposed architecture consists of (i) a perceptive module
that extracts a hierarchical and temporally consistent representation of the
observation at multiple levels, (ii) a deductive module for determining the
relational connection between neurons at each level, and (iii) a statistical
module that can predict the future by conditioning on the temporal
distributional estimation. Our model is demonstrated to be effective in
identifying the componential interactions with limited observation and stable
in long-term future predictions experimented with diverse physical systems.
