Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology.

Automated summary

This article reviews the recent advances in analogue and reservoir computing driven by the properties and energy of water waves. It suggests that these research areas have the potential to bring artificial intelligence closer to rural areas, allowing them to benefit from novel technologies that are already present in large cities. The physical reservoir computing systems discussed in the article can be used for designing and optimizing power grid networks and forecasting energy consumption at local and global scales. Therefore, this review article is of significant importance for readers interested in the innovative practical applications of artificial intelligence and machine learning.