Emerging opportunities and challenges for the future of reservoir computing

Abstract

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines.

Exploring Embodied Intelligence in Soft Robotics: A Review

“…As a unique computing framework, it demonstrates tremendous potential in handling complex time-series data. It is a neural network-based computing approach, with its core being a fixed and randomly generated large-scale neural network known as the "reservoir" [34]. Unlike traditional neural networks, this network does not require comprehensive training but rather maintains its initial random connectivity state, as depicted in Figure 5a.…”

Large sampling intervals for learning and predicting chaotic systems with reservoir computing

“…Recently, reservoir computing (RC), a kind of RNN method, has attracted a lot of attention and has been widely used in the prediction of chaotic systems [11][12][13][14][15][16], and serves as digital twins of various dynamical systems, such as the oscillator models for synchronization [17][18][19] and Lorenz-96 climate network [20]. Many improvements of the method have been proposed for different tasks and dynamical systems, such as the parallel reservoir for spatiotemporal systems [13], and the parameter-aware reservoir for prediction of phase transitions of complex systems [21].…”