Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

Encoding individual behavioral traits into a low-dimensional latent representation enables the accurate prediction of decision-making patterns across distinct task conditions.

This study presents a transfer learning–based method for predicting train-induced environmental vibration. The method applies ...

Researchers have developed a hybrid CFD-neural network model for predicting TAIs in hydrogen-fueled turbines, improving ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

Accurate prediction of breakthrough extruding force is very important for extrusion production, especially for the large-scale extrusion process, which directly affects the production costs and safety ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...