As winter snowstorms, high winds and freezing temperatures caused myriad power outages throughout the United States in late December 2022, an obvious question was raised: Why can’t the energy grid handle such emergencies?

Argonne researchers collaborate on developing a new scheduler for smart city applications.

Argonne researchers develop new approach for cutting large quantum circuits into smaller subcircuits, enabling execution on smaller quantum devices.

Scientists develop multifidelity approach for reliability analysis of aerospace systems.

Cardinal, an open-source simulation software package for high-fidelity multiphysics solutions, was named a finalist for the 2022 R&D 100 awards.

Simulation has become a key part of scientific discovery – complementing experiment, helping guide engineering designs and providing insight into complex problems infeasible in the lab.

Wind farms are increasingly gaining attention as an alternative energy source. But the wakes generated by the wind turbines lead to energy losses, significantly degrading the efficiency of wind power plants.

Today’s computers are providing enormous power, enabling scientists to tackle complex problems previously considered intractable.

The final workshop report for the 2021 Randomized Algorithms for Scientific Computing (RASC) workshop has been released.

Argonne National Laboratory has a long and distinguished record of working with special functions.

Can one effectively upscale x-ray images from nanoimaging to macroscopic specimens within reasonable imaging times?

In the introduction to the July 2021 special issue of the journal SoftwareX, Keahey and her coeditors present a strong argument for giving software the respect it deserves – and the funding that must accompany it.

In a study published in a special issue of the journal Atmosphere, a team of researchers from Argonne National Laboratory presented a novel ground-based approach using machine learning for estimating cloud cover.

Kate Keahey, a senior computer scientist at Argonne National Laboratory and senior scientist at the University of Chicago Consortium for Advanced Science and Engineering, recently argued for a ​“silver lining” in the internet cloud environment.

Researchers from the University of Virginia, Google Inc., and Argonne National Laboratory have teamed together to tackle a long-standing problem in epidemiology: how to predict the spread of infectious diseases.

Three papers coauthored by researchers in the Mathematics and Computer Science Division at Argonne National Laboratory were highlighted recently on the U.S. Department of Energy Exascale Computing Project website.

Curve fitting is a fundamental problem in applications such as computer-aided design and is being explored in data analysis and compression in large-scale simulations.

Researchers from Argonne National Laboratory, together with colleagues from George Mason University and Ohio State University, have devised an automated method that identifies the optimal locations of rain gauges.