In 1982 physicist John Hopfield translated this theoretical neuroscience concept into the artificial intelligence realm, with the formulation of the Hopfield network. In doing so, not only did he ...

Artificial neural networks are widely used in the identification and control of complex systems. However, the network model which is based on neuron nodes, activation functions, and network weights is ...

In this study, we present a novel QM-polarizable water model that incorporates dynamical atomic charges and charge transfer exclusively trained on QM data acquired at the ab initio MP2 level of theory ...

Researchers have developed a transfer learning-enhanced physics-informed neural network (TLE-PINN) for predicting melt pool morphology in selective laser melting (SLM). This novel approach ...

This article proposes a novel parametric modeling technique incorporating a joint polynomial-transfer function with neural networks (short for neuro-PTF) for electromagnetic (EM) behaviors of ...

To achieve satisfactory rolling bearing fault diagnosis of the new energy vehicle, a transfer-based deep neural network (DNN-TL) is proposed in this study by combining the benefits of both deep ...

Convolutional Neural Networks Overview of Convolutional Neural Networks In this study, four representative CNN models were tested, including the first deep-CNN (AlexNet), a significantly improved CNN ...