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Neural Networks for Lorenz Map Prediction: A Trip Through Time

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arxiv 1903.07768 v5 pith:C7RMS6AP submitted 2019-03-18 cs.LG stat.ML

Denisa Roberts This is my paper · ORCID
classification cs.LGstat.ML
keywords lorenzarticlelearningnetworksneuralpredictionaheadcanonical
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data

    nlin.CD 2025-07 conditional novelty 6.0 of 10

    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.

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