Developing accurate dynamic models for various systems is crucial for optimization, control,
fault diagnosis, and prognosis. Recent advancements in information technologies and …

Neural networks for deep-learning applications, also called artificial neural networks, are
important tools in science and industry. While their widespread use was limited because of …

Activation functions play an important role in deep learning models by introducing non-
linearity to the output of a neuron, enabling the network to learn complex patterns and non …

Gravitational waves from the coalescences of black holes and neutron stars afford us the
unique opportunity to determine the sources' properties, such as their masses and spins …

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment
selection still proceeds via “trial and error”. Given the varied presentation of MDD and …

In recent years, demand for electric energy has steadily increased; therefore, the integration
of renewable energy sources (RES) at a large scale into power systems is a major concern …

The subject of investigation in this paper is the modeling of adiabatic thermally expanded
hyperelasticity. Through the use of neural networks Cauchy stresses were predicted from …

Neural networks have become standard tools in the analysis of data, but they lack
comprehensive mathematical theories. For example, there are very few statistical …

Efficient integration of wind energy requires accurate wind power forecasting. This prediction
is critical in optimising grid operation, energy trading, and effectively harnessing renewable …

Neural networks are becoming increasingly popular in applications, but our mathematical
understanding of their potential and limitations is still limited. In this paper, we further this …