Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma ...

According to Lehigh University, an NSF-funded project led by researcher Parisa Khodabakhshi aims to streamline machine learning models that incorporate physical laws, facilitating alloy design for ...

Machine learning models are being used more and more widely. However, they need a lot of training data to deliver good results. In industrial applications, this wealth of data is often not available ...

Festo’s Eric Rice explains two commonly used concepts “(software-defined automation” and “function integration”) in simple terms before contextualizing how Festo applies these concepts. The goal of ...

Recent developments in machine learning techniques have been supported by the continuous increase in availability of high-performance computational resources and data. While large volumes of data are ...

Jeannine Barganier’s father ran the ride in the 1940s, met his wife between the soaring horses and passed its magic to the next four generations. As a 501(c)3 nonprofit news organization with a hybrid ...

Abstract: Human-machine function allocation or adaptive automation is an important means to achieve human-machine integration in human-machine collaboration systems. One of the purposes of ...

Abstract: Arctic sea ice thickness is vital for evaluating Arctic sea ice loss. The study aims to investigate Arctic sea ice thickness prediction using PhySIT, a physics-informed machine learning ...

Physics-based modeling has long been instrumental in advancing our understanding of physiological systems, including cardiovascular, neurovascular, pulmonary, and gastrointestinal systems. These ...