This repository contains the code for the paper "Sensing with shallow recurrent decoder networks" by Jan P. Williams, Olivia Zahn, and J. Nathan Kutz. SHallow REcurrent Decoders (SHRED) are models that learn a mapping from trajectories of sensor measurements to a high-dimensional, spatio-temporal state. For an example use of SHRED, see the iPython notebook example.ipynb.

The datasets considered in the paper "Sensing with shallow recurrent decoder networks" consist of sea-surface temperature (SST), a forced turbulent flow, and atmospheric ozone concentration. Cloning this repo will download the SST data used. Details for accessing the other datasets can be found in the supplement to the paper.