Engineering simulations traditionally rely on finite element methods, which are accurate but computationally expensive, while scientific machine learning offers faster, data-driven alternatives. The ...

This work includes articles from the Arthur M. Sackler Colloquium on the Scientific Examination of Art: Modern Techniques in Conservation and Analysis held at the National Academy of Sciences Building ...

As scientific instruments and the literature generate ever larger volumes of data, machine learning (ML) has become essential for organizing, analyzing and interpreting complex information. This ...