Model-Free Neural State Estimation in Nonlinear Dynamical Systems: Comparing Neural and Classical Filters

ArXi:2601.21266v2 Announce Type: replace Neural network models are increasingly used for state estimation in control and decision-making problems, yet it remains unclear to what extent they behave as principled filters in nonlinear dynamical systems. Unlike classical filters, which rely on explicit knowledge of system dynamics and noise models, neural estimators can be trained purely from data without access to the underlying system equations.