Phase2vec: Dynamical systems embedding with a physics-informed convolutional network

Understand

Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into universal equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible.

• Predicting these classes from data remains an essential open challenge in computational physics at which existing time-series classification methods struggle.
• Here, we propose, \texttt{phase2vec}, an embedding method that learns high-quality, physically-meaningful representations of 2D dynamical systems without supervision.
• Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physically-informed vector field reconstruction loss.