Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Accurate multi-step-ahead wind speed (WS) and wind power (WP) forecasting are critical to
the scheduling, planning, and maintenance of wind farms. Previous forecasting methods …

This article aims to study fixed-time projective lag synchronization (FXPLS) and preassigned-
time projective lag synchronization (PTPLS) of hybrid inertial neural networks (HINNs) with …

It is unquestionable that time series forecasting is of paramount importance in many fields.
The most used machine learning models to address time series forecasting tasks are …

This paper discusses the chaos prediction of nonlinear systems using various neural
networks based on the modified substructure data-driven modeling architecture. In the …

Sparse system identification is the data-driven process of obtaining parsimonious differential
equations that describe the evolution of a dynamical system, balancing model complexity …

This article mainly addresses the problems of fixed-time stabilization (FTS) and fixed-time
projective synchronization (FTPS) for the chaotic inertial neural networks (INNs) with mixed …

The benefits of noise to applications of nonlinear dynamical systems through mechanisms
such as stochastic and coherence resonances have been well documented. Recent years …

This research constructed the innovative Local-EMD-WaveNet, a multi-channel neural
network model, specifically designed for the prediction of significant wave height (SWH) at a …

Describing the dynamical properties of explored systems, one finds the need to distinguish
between various types of trajectories. The nature of trajectories is often split into regular and …