Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty. Researchers have developed a lightweight machine learning framework that ...

Another theory held that the forces between two particles falls off exponentially in direct relationship to the distance between two particles and that the factor by which it drops is not dependent on ...

Researchers employ machine learning to more accurately model the boundary layer wind field of tropical cyclones. Conventional approaches to storm forecasting involve large numerical simulations run on ...

Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as weather patterns, recorded speech or stock market trends. Classical ...

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 ...

Parisa Khodabakhshi is an assistant professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. Prior to joining the Lehigh faculty ...

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...

Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

For decades, scientists have relied on structure to understand protein function. Tools like AlphaFold have revolutionized how researchers predict and design folded proteins, allowing for new ...