RIT researchers publish a paper in Nature Scientific Reports on a new tree-based machine learning algorithm used to predict chaos.

Granular matter is all around us. Examples include sand, rice, nuts, coffee and even snow. These materials are made of solid particles that are large enough not to experience thermal fluctuations.

A team of New York University computer scientists has created a neural network that can explain how it reaches its predictions. The work reveals what accounts for the functionality of neural networks ...

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, ...

Deep neural networks are becoming increasingly popular in more and more industries, and while recent developments in networks designed to play Go and online video games have excited the attention of ...

Illustration of a strike-slip fault at a tectonic plate boundary. The tectonic plates move parallel to each other, leading to so-called strike-slip earthquakes with relatively little deformation.

In disaster mitigation planning for future large earthquakes, seismic ground motion predictions are a crucial part of early warning systems. The way the ground moves depends on how the soil layers ...

Results from neural networks support the idea that brains are “prediction machines” — and that they work that way to conserve energy. How our brain, a three-pound mass of tissue encased within a bony ...