Universal Learning of Nonlinear Dynamics
The work introduces Observation Spectral Filtering (OSF), an algorithm that learns any observable nonlinear dynamical system with finitely many marginally stable modes by directly mapping past observations to future predictions. It reframes learning as competing against idealized linear predictors, using control theory concepts like the Luenberger program to quantify system learnability.