The active learning framework accelerates identification of anomalies in code and strengthens scientific workflows without the need for large sets of training data.
SQUEEZE (SZ) compresses data while preserving quality. Argonne researchers and SZ developers, Sheng Di and Franck Cappello, share development insights.
A team of researchers from Argonne National Laboratory has devised a new approach that significantly reduces the computational cost of x-ray ptychography, while maintaining high resolution.
In a recent article in Significance magazine, researchers addressed some of the factors affecting energy demands, with a particular focus on the role of statistics in energy forecasting.
