Ultra-chaos of square thin plate in low Earth orbit

Zhang, Bo; Yang, Yu; Liao, Shijun

doi:10.1007/s10409-023-23428-x

Acta Mech. Sin.

2024

DOI: 10.1007/s10409-023-23428-x

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Ultra-chaos of square thin plate in low Earth orbit

Bo Zhang,

Yu Yang,

Shijun Liao

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Ultra-Chaos: A Great Challenge for Machine Learning and AI

Yang,

Liao

2024

Int. J. Bifurcation Chaos

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Machine Learning (ML) is highly data dependent. But all data contain noise. Especially for chaotic systems, due to the famous “butterfly-effect”, small disturbances can grow exponentially to the same order of magnitude as the physical solution so that numerical simulations of chaos become badly polluted. A fundamental and open problem is how data noise in chaotic systems influences short and long-term predictions of ML based on such badly polluted data. Ultra-chaos, whose statistics are sensitive to small disturbances, is a new concept in chaos theory. In this paper, the influence of data noise on ML of chaos is investigated in detail by comparing predictions with three ML models based on a “clean” dataset given by Clean Numerical Simulation (CNS), and a “badly polluted” dataset given by the traditional Runge–Kutta method. It is found that neither data noise nor ML prediction deviation influence the long-term statistics of ML predictions for normal-chaos. However, data noise has a significant influence on the statistics of ML predictions for ultra-chaos: this might pose a great challenge for ML that is a part of AI.

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Ultra-Chaos: A Great Challenge for Machine Learning and AI

Yang,

Liao

2024

Int. J. Bifurcation Chaos

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show abstract

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Ultra-chaos of square thin plate in low Earth orbit

Cited by 1 publication

References 39 publications

Ultra-Chaos: A Great Challenge for Machine Learning and AI

Ultra-Chaos: A Great Challenge for Machine Learning and AI

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