Learning minimal representations of dynamical systems is essential for mathematical modelling and prediction in science and engineering. Floryan and Graham propose a deep learning framework able to estimate accurate global dynamical models by sewing together multiple local representations learnt from high-dimensional time-series data.
- Daniel Floryan
- Michael D. Graham
