Explicit construction of recurrent neural networks effectively approximating discrete dynamical systems

This work addresses the challenge of modeling discrete dynamical systems for researchers in computational mathematics and machine learning, though it appears incremental as it builds on existing approximation theory.

The authors tackled the problem of approximating arbitrary bounded discrete time series from recursive dynamical systems by providing an explicit construction of recurrent neural networks that effectively approximate these systems.