# Analogue and Physical Reservoir Computing Using Water Waves

arXiv: https://arxiv.org/abs/2306.09095 · HTML: https://mlsanity.com/paper/2306.09095/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 159 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Wave Physics as an Analog Recurrent Neural Network](https://mlsanity.com/paper/1904.12831/) (2019)
- [Surrogate gradients for analog neuromorphic computing](https://mlsanity.com/paper/2006.07239/) (2020)
- [Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius](https://mlsanity.com/paper/1910.04426/) (2019)
- [Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons](https://mlsanity.com/paper/1912.07044/) (2019)
- [A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systems](https://mlsanity.com/paper/1906.04608/) (2019)
- [Physics-Informed Echo State Networks](https://mlsanity.com/paper/2011.02280/) (2020)
- [A perspective on physical reservoir computing with nanomagnetic devices](https://mlsanity.com/paper/2212.04851/) (2022)
- [Harnessing disordered quantum dynamics for machine learning](https://mlsanity.com/paper/1602.08159/) (2016)
- [Neural Echo State Network using oscillations of gas bubbles in water](https://mlsanity.com/paper/2112.11592/) (2021)
- [Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs](https://mlsanity.com/paper/1805.03033/) (2018)
- [Efficient implementations of echo state network cross-validation](https://mlsanity.com/paper/2006.11282/) (2020)
- [A New Paradigm of Reservoir Computing Exploiting Hydrodynamics](https://mlsanity.com/paper/2302.01978/) (2023)
- [Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept](https://mlsanity.com/paper/2303.01801/) (2023)
- [Stabilising falling liquid film flows using feedback control](https://mlsanity.com/paper/1506.01593/) (2015)
- [Musical creativity enabled by nonlinear oscillations of a bubble in water](https://mlsanity.com/paper/2304.00822/) (2023)

## Built on by (most cited first, 16 of 16)

- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Quantum-Inspired Neural Network Model of Optical Illusions](https://mlsanity.com/paper/2312.03447/) (2023)
- [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024)
- [Quantum-tunnelling deep neural network for optical illusion recognition](https://mlsanity.com/paper/2407.11013/) (2024)
- [The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics](https://mlsanity.com/paper/2310.00267/) (2023)
- [Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data](https://mlsanity.com/paper/2402.03319/) (2024)
- [Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations](https://mlsanity.com/paper/2412.08010/) (2024)
- [Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectives](https://mlsanity.com/paper/2503.04978/) (2025)
- [Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime](https://mlsanity.com/paper/2504.07221/) (2025)
- [TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning](https://mlsanity.com/paper/2505.00933/) (2025)
- [Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing](https://mlsanity.com/paper/2506.04523/) (2025)
- [Traveling Waves Integrate Spatial Information Through Time](https://mlsanity.com/paper/2502.06034/) (2025)
- [Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications](https://mlsanity.com/paper/2503.07681/) (2025)
- [Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition](https://mlsanity.com/paper/2507.15158/) (2025)
- [BioOSS: A Bio-Inspired Oscillatory State System with Spatio-Temporal Dynamics](https://mlsanity.com/paper/2510.10790/) (2025)
- [BOND: License to Train with Black-Box Functions](https://mlsanity.com/paper/2510.25074/) (2025)

## Abstract (arXiv)

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 unique physical properties and energy of water waves. It also demonstrates that analogue and reservoir computing hold the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that already work in large cities but are not readily available and suitable for regional communities.