Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM

20 June 2019

Papers citing "Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM"

7 / 7 papers shown

Title
Simplicity bias, algorithmic probability, and the random logistic map B. Hamzi K. Dingle 23 3 0 31 Dec 2023
Variability of echo state network prediction horizon for partially observed dynamical systems Ajit Mahata Reetish Padhi A. Apte 26 1 0 19 Jun 2023
One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels Matthieu Darcy B. Hamzi Giulia Livieri H. Owhadi P. Tavallali 36 26 0 24 Sep 2022
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics A. Racca Luca Magri OOD AAML 16 60 0 09 Feb 2021
Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems Allen G. Hart J. Hook Jonathan H.P Dawes 27 46 0 14 May 2020
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model D. Gagne H. Christensen A. Subramanian A. Monahan AI4CE BDL 44 139 0 10 Sep 2019
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 236 7,906 0 13 Jun 2015