Pantelis R. Vlachas

TL;DR: In this paper, a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks is introduced. But the LSTM neural networks perform inference of highdimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor.

TL;DR: This study establishes that RNNs are a potent computational framework for the learning and forecasting of complex spatiotemporal systems.

TL;DR: A novel hybrid framework that complements an imperfect reduced order model, with data-streams that are integrated though a recurrent neural network (RNN) architecture is developed, showing that the blended approach has improved performance compared with methods that use either data streams or the imperfect model alone.

TL;DR: A data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks and a hybrid architecture, extending the LSTM with a mean stochastic model (MSM–L STM), is proposed to ensure convergence to the invariant measure.

TL;DR: This study confirms that RNNs present a potent computational framework for the forecasting of complex spatio-temporal dynamics.