Less instrumentation. More insight. Physics-informed virtual sensors are shifting condition monitoring from isolated pilots ...

An interatomic potential is a set of mathematical rules that describes the complex dance of forces between atoms — how atomic ...

Foams are everywhere: soap suds, shaving cream, whipped toppings and food emulsions like mayonnaise. For decades, scientists ...

Researchers from the University of Chinese Academy of Sciences and collaborating institutions have developed a novel ...

Physics-informed Neural Networks (PINNs) have emerged as a promising approach to address some of the limitations of classical Neural Networks (NNs) in science and engineering applications. In ...

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 ...

Abstract: We employ physics-informed machine learning to investigate nonlinear fiber transmission with statistical polarization rotations. Our approach discerns difference between nonlinear factors of ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

In the world of particle physics, where scientists unravel the mysteries of the universe, artificial intelligence (AI) and machine learning (ML) are making waves with how they're increasing ...