Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum ...

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

A team of researchers at Penn State have devised a new, streamlined approach to designing metasurfaces, a class of engineered ...

In our increasingly electrified world, supercapacitors have emerged as critical components in transportation and renewable energy systems, prized for their remarkable power density, cycling stability, ...

Physics-informed neural networks are faster and more accurate at predicting space junk trajectories than conventional methods, says Sierra Space. Credit: Alamy Stock Photo Sierra Space says it can ...

A new study suggests that the brain processes information with high efficiency by synchronizing the physical wiring of neural ...

They trained a neural network on 14 million 30-second long samples of sea surface elevation measurements from 172 buoys located off the shores of the continental United States and Pacific Islands. The ...

Scientists found that neural networks cannot yet forecast 'gray swan' weather events, which might not appear in existing training data but could still happen -- like 200-year floods or massive ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

This allowed them to determine the extent to which the participants' neural responses when watching videos resembled those of other students who they did not yet know, but with whom they later became ...