These codes are provided in addition to the paper named "Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM" Link: https://arxiv.org/pdf/1906.08829v3 In this paper, the performance of three deep learning methods for predicting short-term evolution of a multi-scale spatio-temporal Lorenz 96 system is examined. Here includes the code of two methods ANN and RC-ESN. Also, a data set of normalized Lorenz 96 equation is provided for execution. The ANN code is supported by keras and backend on TensorFlow
We are thankful to Pantelis Vlachas and Jaideep Pathak for sharing the open-source codes for the comparisons. We are grateful to ashesh chattopadhyay for sharing the open-source code and helping us execution part of the code. We also thank our guides for supporting us. 1.Mr. Manmeet Singh, Scientist C, IITM Pune 2.Dr. Sooraj KP, Scientist E, IITM Pune 3. Dr. Chirag Dhara, Research Associate, IITM Pune
Contributors: 1.Nandini Jain 2.Mrunal Kawadkar