Accuracy of neural networks for the simulation of chaotic dynamics: Precision of training data vs precision of the algorithm.

We explore the influence of precision of the data and the algorithm for the simulation of chaotic dynamics by neural network techniques. For this purpose, we simulate the Lorenz system with different precisions using three different neural network techniques adapted to time series, namely, reservoir computing [using Echo State Network (ESN)], long short-term memory, and temporal convolutional network, for both short- and long-time predictions, and assess their efficiency and accuracy. Our results show that the ESN network is better at predicting accurately the dynamics of the system, and that in all cases, the precision of the algorithm is more important than the precision of the training data for the accuracy of the predictions. This result gives support to the idea that neural networks can perform time-series predictions in many practical applications for which data are necessarily of limited precision, in line with recent results. It also suggests that for a given set of data, the reliability of the predictions can be significantly improved by using a network with higher precision than the one of the data.