Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network
Problem
Predicting the short-term evolution and long-term statistics of a multiscale chaotic Lorenz 96 system when only the large-scale variables are observable and the smaller-scale processes are never known.
Why it matters
Unobserved small-scale processes make chaotic multiscale systems fundamentally hard to predict.
What the AI did
Three machine-learning methods (echo state network/reservoir computing, ANN, RNN-LSTM) were trained on only the large-scale X variables to forecast trajectories and reproduce long-term statistics.
Results
RC-ESN substantially outperforms ANN and RNN-LSTM, accurately forecasting chaotic trajectories for hundreds of solver time steps (several Lyapunov timescales); RNN-LSTM beats ANN
Impact
Demonstrates reservoir computing as an effective data-driven predictor for chaotic multiscale dynamics from partial observations.
Record
| Venue | Nonlinear processes in geophysics |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian |
| Citations | 168 (OpenAlex, at import time) |
| References | 85 |
| Model or method | RC-ESN (echo state network), ANN, RNN-LSTM |
| Organization | not stated in source |
| Primary source | https://doi.org/10.5194/npg-27-373-2020 |
| Open access | gold |
| Keywords | reservoir computing, Lorenz 96, chaotic systems, machine learning, prediction |
Importance rating
real but narrower advance
Demonstrates ML prediction of chaotic multiscale dynamics on a toy Lorenz 96 system; abstract only discusses implications for weather/climate, no scale stated.
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