Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …

The primary neural networks' decision-making units are activation functions. Moreover, they
evaluate the output of networks neural node; thus, they are essential for the performance of …

A mechanics‐informed artificial neural network approach for learning constitutive laws
governing complex, nonlinear, elastic materials from strain–stress data is proposed. The …

Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

In the context of music production, distortion effects are mainly used for aesthetic reasons
and are usually applied to electric musical instruments. Most existing methods for nonlinear …

Accurate and detailed data are vital for fundamental understanding of turbulent combustion.
However, studies of turbulent combustion often suffer from measurement sparsity or high …

The choice of activation function is essential for building state-of-the-art neural networks. At
present, the most widely-used activation function with effectiveness is ReLU. However …

Parametric activation functions, such as PReLU and PELU, are a relatively new subdomain
of neural network nonlinearities. In this paper, we present a comparison of these methods …

Adversarial attacks on convolutional neural networks (CNNs) have been a serious concern
in recent years, as they can cause CNNs to produce inaccurate predictions. Through our …

The most widely used activation functions in current deep feed-forward neural networks are
rectified linear units (ReLU), and many alternatives have been successfully applied, as well …