Chaos Computing at AI Age

Dear Colleagues,

The quantum theory, the theory of relativity, and chaos theory are the three fundamental discoveries of the 20th century. However, the worldwide theoretical interest in the traditional chaos seems to wane except for several few-and-far-between practical application attempts. In recent years, there has been a notable resurgence in chaotic research with the fast development of artificial intelligence (AI), specifically deep learning technology. For example, the determination of chaotic characteristics can be conducted innovatively by effectively using the deep neural network pattern recognition of the graph generated from the time-series data. On the other hand, it is well conventionally acknowledged that there is no possibility of prediction for a chaotic system. Nevertheless, the appealing emergence of computing chaos, which includes machine learning and its algorithm called reservoir computing, can open up an enlightening perspective for future development. For instance, one can improve predictions of the Karamoto-Sivashinsy equation eight times further ahead than in previous methods. Combining machine learning and traditional model-based predictions, one could obtain accurate predictions twelve Lyapunov times. Therefore, the traditional chaotic investigation thinking should keep pace with the times. In this Special Issue, we cordially invite colleagues to submit their recent results on the chaos by using modern AI approaches. What step the AI can lead to the chaotic determination and prediction, its mechanism, and the application condition and scope of application are all preferred in the issue. We believe that your contribution will benefit the chaos research community as a whole. Potential topics include but are not limited to the following:

• Machine learning and chaos;
• Chaos and swarm intelligence;
• Chaos in recurrent neural networks;
• Chaos-based optimization algorithms;
• AI-based techniques in chaotic dynamics prediction;
• Machine learning in chaos-based encryption algorithms;
• Security enhancement for chaos- and AI-based encryption algorithms.

Prof. Dr. Mingwei Sun
Prof. Dr. Li Xiong
Prof. Dr. Shijian Cang
Dr. Hao Sun
Guest Editors

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• machine learning and chaos
• chaos and swarm intelligence
• chaos in recurrent neural networks
• chaos-based optimization algorithms
• AI-based techniques in chaotic dynamics prediction
• machine learning in chaos-based encryption algorithms
• security enhancement for chaos- and AI-based encryption algorithms