Traditional statistical, physical, and correlation models for chaotic time series prediction
have problems, such as low forecasting accuracy, computational time, and difficulty …

This paper investigates the use of a data-driven method to model the dynamics of the
chaotic Lorenz system. An architecture based on a recurrent neural network with long and …

With the rapid development of solar energy, the photovoltaic (PV) module fault detection
plays an important role in knowing how to enhance the reliability of the solar photovoltaic …

Josephson junction resonators are the devices which exhibit complex behaviours as a
consequence of their inductive properties. Even though the insulating medium between …

The characteristics of a nonlinear dynamical system within chaotic system is more intensely
studied recently, due to many real-world applications of the nonlinear chaotic system are …

This work builds off the manifold-constrained Gaussian process inference (MAGI) method for
Bayesian parameter inference and trajectory reconstruction of ODE-based dynamical …

Predicting earthquake occurrences in time series data remains a challenging task in
seismology. NARX (Nonlinear Autoregressive with eXogenous input) neural networks have …

Chaotic dynamics are the paradigm of complex and unpredictable evolution due to their built-
in feature of amplifying arbitrarily small perturbations. The forecasting of these dynamics has …