There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this
date PINNs have not been successful in simulating dynamical systems whose solution …

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving
partial differential equations (PDEs) in a variety of domains. While previous research in …

Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning
framework for approximating numerical solutions to partial differential equations (PDEs) …

Blind deconvolution (BD) has been demonstrated to be an efficacious approach for
extracting bearing fault-specific features from vibration signals under strong background …

Recent years have witnessed a growth in mathematics for deep learning—which seeks a
deeper understanding of the concepts of deep learning with mathematics and explores how …

The smooth interaction between the pantograph and the catenary is crucial for the
operational safety of railway vehicles. Coupled dynamic models of the pantograph–catenary …

The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple
sophisticated DNN libraries. On the contrary, although some work have proved that …

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this
date PINNs have not been successful in simulating dynamical systems whose solution …

Inspired by the diversity of biological neurons, quadratic artificial neurons can play an
important role in deep learning models. The type of quadratic neurons of our interest …