  The detection of anomalies or transitions in complex dynamical systems is of
critical importance to various applications. In this study, we propose the use
of machine learning to detect changepoints for high-dimensional dynamical
systems. Here, changepoints indicate instances in time when the underlying
dynamical system has a fundamentally different characteristic - which may be
due to a change in the model parameters or due to intermittent phenomena
arising from the same model. We propose two complementary approaches to achieve
this, with the first devised using arguments from probabilistic unsupervised
learning and the latter devised using supervised deep learning. Our emphasis is
also on detection for high-dimensional dynamical systems, for which we
introduce the use of dimensionality reduction techniques to accelerate the
deployment of transition detection algorithms. Our experiments demonstrate that
transitions can be detected efficiently, in real-time, for the two-dimensional
forced Kolmogorov flow, which is characterized by anomalous regimes in phase
space where dynamics are perturbed off the attractor at uneven intervals.
