Chaos theory initially originated from the simulation of dynamic systems in the natural world, such as weather prediction. Computational chaos was then proposed, since it was found that chaotic behaviour exists not only in natural dynamic systems, but also in the process of discretisation. The perspective of computational chaos includes scenarios where the original system shows periodicity, but its computational simulation is chaotic, as well as the inverse situation in which the system is chaotic, while the discretised version presents regularity. There are many works investigating the relationship between chaos theory and computational simulation. The finite precision of computer arithmetic strongly affects the final result of the simulation of a dynamic system, and a trivial change in the arithmetic computation is also able to modify the structure of the pseudo-orbits significantly.

Different from traditional natural nonlinear dynamics, in recent years, advances in terms of bioinformatics, big data, social networks, and deep learning have demonstrated new kinds of complex phenomena. Nonetheless, the discretisation and arithmetic simulation of these complex dynamics and corresponding chaotic features have not been comprehensively reported. Chaos theory is not only a mathematical art, it also has a great potential for practical engineering problems. Some practical applications of chaos include behaviour prediction in complex systems, electronic circuit design, biological system analysis, random number generators, information encryption, synchronisation for communication and control, parameter estimation of nonlinear systems, and modelling, among others. Most of these applications benefit from the power of chaos theory in terms of modelling an irregular system with a deterministic equation, yet the deterministic equation generally has very high sensitivity to the initial condition. Currently, a lot of irregular systems linking man and man, man and machine, and machine and machine are being proposed, and chaos theory is believed to have great potential for solving engineering problems in these complex systems.

The goal of this Special Issue is to provide a platform for state-of-the-art achievements in computational chaos in novel complex systems and explore the potential of chaos for engineering applications. Original research and review articles are both welcome.

Potential topics include but are not limited to the following:

• Chaos theory and chaotic systems
• Computational chaos in complex systems
• Design of chaos circuits and systems
• Prediction using chaotic theory
• Chaos-based information security
• Chaos-based random number generators
• Fractional order chaotic systems
• Deep learning and chaos
• Chaos in neural networks
• Chaos in social, physical, and biological systems
• Chaos and fractals