Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph. Data-driven approximations of ...

This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating ...

A tandem neural network capable of inferring key physical parameters of semiconductor materials from simple transistor measurements has been developed, as reported by researchers from the Institute of ...

A key objective of several neuroscience studies is to understand and model how the dynamics of distinct populations of neurons give rise to specific human and animal behaviors. Many existing methods ...

Two-year-old startup Mindbeam AI Inc. today released an open-source artificial intelligence inference framework designed to ...

“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 ...

A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has ...

Neurologists use millisecond-level M/EEG tracking to prove the human brain and AI language models organize and predict language using parallel processing principles.

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

A hunk of material bustles with electrons, one tickling another as they bop around. Quantifying how one particle jostles others in that scrum is so complicated that, beginning in the 1990s, physicists ...

Using a novel simulation model based on machine learning, an international research team at GSI/FAIR has succeeded in gaining a deeper understanding of element formation in stellar events such as ...

A machine learning-powered simulation is giving researchers a new window into the processes that create some of the universe’s heaviest elements.