Artificial neural networks (ANNs) have proven to be extremely useful for solving problems such as classification, regression, function estimation and dimensionality reduction.However, it turns out ...

Recurrent neural networks, or RNNs, are a style of neural network that involve data moving backward among layers. This style of neural network is also known as a cyclical graph .

Pooling layers, which downsample the output of the convolutional layers to lower the computational cost and increase the network’s capacity to generalize to new inputs, are frequently included ...

Pooling (downscaling) layers run from 1D to 3D and include the most common variants, such as max and average pooling. Locally connected layers act like convolution layers, except that the weights ...

It consists of two main types of layers: convolutional layers and pooling layers, which are both utilized to great effect in the training of neural networks. The convolutional layer uses a ...

Most convolutional neural networks use pooling layers to gradually reduce the size of their feature maps and keep the most prominent parts. Max-pooling, which is currently the main type of pooling ...

As more convolutional and pooling layers are added, the feature maps zoom out and can detect complicated things such as faces and objects. Each layer of the neural network encodes specific ...

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 ...