The search for next-generation electronic materials often starts with studying the Fermi surface, which serves as a map of a ...

Now, artificial intelligence (AI) tools are providing powerful new ways to address long-standing problems in physics. “The ...

From smarter hypothesis testing with e-values to AI systems that model emotions, Bayesian methods and probabilistic reasoning are transforming how machines—and humans—make decisions under uncertainty.

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

Researchers at Stevens Institute of Technology used machine learning tools and social network theory—the study of how people connect with each other—to better understand how people interact online.

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

The Army is creating a dedicated artificial intelligence and machine-learning career field for officers as it pushes to integrate AI more deeply into its operations. The new 49B specialty establishes ...

Abstract: Multiple instance learning (MIL) has shown prominent success in analyzing whole slide histopathology images (WSIs). However, existing MIL methods often suffer from overfitting due to weak ...

ABSTRACT: Machine learning (ML) has become an increasingly central component of high-energy physics (HEP), providing computational frameworks to address the growing complexity of theoretical ...