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 …

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural
network in which neurons are randomly connected. Once initialized, the connection …

Digital twins have attracted a great deal of recent attention from a wide range of fields. A
basic requirement for digital twins of nonlinear dynamical systems is the ability to generate …

Model-free reconstruction of bifurcation diagrams of Chua's circuits using the technique of
parameter-aware reservoir computing is investigated. We demonstrate that (1) reservoir …

An abundance of complex dynamical phenomena exists in nature and human society,
requiring sophisticated analytical tools to understand and explain. Causal analysis through …

Multifunctional biological neural networks exploit multistability in order to perform multiple
tasks without changing any network properties. Enabling artificial neural networks (ANNs) to …

Complex and nonlinear dynamical systems often involve parameters that change with time,
accurate tracking of which is essential to tasks such as state estimation, prediction, and …

Anticipating a tip** point, a transition from one stable steady state to another, is a problem
of broad relevance due to the ubiquity of the phenomenon in diverse fields. The steady-state …

In this article, we introduce a decentralized digital twin (DDT) modeling framework and its
potential applications in computational science and engineering. The DDT methodology is …

Traditional neural network models of associative memories were used to store and retrieve
static patterns. We develop reservoir-computing based memories for complex dynamical …