The forecasting of high-dimensional, spatiotemporal nonlinear systems has made
tremendous progress with the advent of model-free machine learning techniques. However …

Physics-based computing exploits nonlinear or disorder-induced complexity, for example, to
realize energy-efficient and high-throughput computing tasks. A particularly difficult but …

Extreme events generated by complex systems have been intensively studied in many fields
due to their great impact on scientific research and our daily lives. However, their prediction …

In real-world dynamical systems, technical limitations may prevent complete access to their
dynamical variables. Such a lack of information may cause significant problems, especially …

Forecasting the dynamics of chaotic systems from the analysis of their output signals is a
challenging problem with applications in most fields of modern science. In this work, we use …

Understanding and modeling the dynamics of multiscale systems is a problem of
considerable interest both for theory and applications. For unavoidable practical reasons, in …

We demonstrate the successful prediction of the continuous intensity time series and
reproduction of the underlying dynamical behaviors for a chaotic semiconductor laser by …

Studying the chaotic dynamics of semiconductor lasers is of great importance for their
applications in random bit generation and secure communication. While considerable effort …

Machine learning approaches have recently been leveraged as a substitute or an aid for
physical/mathematical modeling approaches to dynamical systems. To develop an efficient …

An emerging paradigm for predicting the state evolution of chaotic systems is machine
learning with reservoir computing, the core of which is a dynamical network of artificial …