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Status: Unchecked ×Keyword: Lyapunov time ×Clear all

3 claims from 1 paper

1. Physics and Astronomy › Model Reduction and Neural Networks

   Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study

   Soldatenko and Angudovich · Climate · 2024

   The paper tests unidirectional and bidirectional LSTM networks as surrogate models for a coupled fast–slow Lorenz system standing in for the atmosphere and ocean, and examines forecast accuracy and predictability limits.

   Unchecked3 claims
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   1. UncheckedIn this study, bidirectional LSTM neural networks forecast a chaotic coupled fast–slow system more accurately than unidirectional LSTM networks.“The findings revealed the advantages of bidirectional neural networks over unidirectional ones in terms of forecasting accuracy.”
   2. UncheckedIn this study, with other settings held equal, giving a neural network more neurons per hidden layer tended to make its forecasts more accurate.“All other things being equal, increasing the number of neurons in hidden layers facilitates the improvement of forecast accuracy.”
   3. UncheckedIn a coupled fast–slow Lorenz model, the fast 'atmosphere' part stays predictable for somewhat longer than its Lyapunov time.“The predictability limit of the fast subsystem (“atmosphere”) is somewhat greater than the Lyapunov time.”