Double projection for reconstructing dynamical systems: between stochastic and deterministic regimes

ArXi:2510.01089v2 Announce Type: replace Learning stochastic models of dynamical systems from observed data is of interest in many scientific fields. Here, we propose a new method for this task within the family of dynamical variational autoencoders. The proposed double projection method estimates both the system state trajectories and the noise time series from data. This approach naturally allows us to perform multi-step system evolution and to learn models with a comparatively low-dimensional state space.