Neural Predictors’ Sensitivity and Robustness

The results of the application of deep neural predictors depend on a multitude of factors which compose the experimental settings. We report all the specific information to ensure the reproducibility of a wide number of numerical experiments. A sensitivity analysis on some critical aspects is provided in order to prove the robustness of our setting. Considering the long-term behavior of the predictors, those trained for the one-step forecasting are able to reproduce the statistical properties of the attractor, i.e., the so-called attractor’s climate, whereas the multi-step ones are unsuitable for replicating these statistical properties but provide an accurate forecasting up to several Lyapunov times. Lastly, we provide some remarks on the training procedure of the different predictors and introduce some advanced neural architectures to give an overview of possible advantages/disadvantages with respect to those implemented in this study.