Polynomial regression with 512-bit arithmetic reaches machine-precision forecasting of low-dimensional chaotic systems from noise-free data, far exceeding previous valid prediction times.
Neural Networks for Lorenz Map Prediction: A Trip Through Time
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abstract
In this article the Lorenz dynamical system is revived and revisited and the current state of the art results for one step ahead forecasting for the Lorenz trajectories are published. Multitask learning is shown to help learning the hard to learn z trajectory. The article is a reflection upon the evolution of neural networks with respect to the prediction performance on this canonical task.
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Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data
Polynomial regression with 512-bit arithmetic reaches machine-precision forecasting of low-dimensional chaotic systems from noise-free data, far exceeding previous valid prediction times.