  We present an approach, based on learning an intrinsic data manifold, for the
initialization of the internal state values of LSTM recurrent neural networks,
ensuring consistency with the initial observed input data. Exploiting the
generalized synchronization concept, we argue that the converged, "mature"
internal states constitute a function on this learned manifold. The dimension
of this manifold then dictates the length of observed input time series data
required for consistent initialization. We illustrate our approach through a
partially observed chemical model system, where initializing the internal LSTM
states in this fashion yields visibly improved performance. Finally, we show
that learning this data manifold enables the transformation of partially
observed dynamics into fully observed ones, facilitating alternative
identification paths for nonlinear dynamical systems.
