Topic: Space Science

Researchers are using LLNL’s unclassified supercomputers to delve into complex scientific questions such as uncovering how protein interactions are linked to cancer, shedding light on mysterious dark matter, and understanding the shifting dynamics of seismic waves.

The latest issue of LLNL's magazine marks the 20th anniversary of the Computing Grand Challenge.

In a recent study published in the Astrophysical Journal, LLNL researchers developed an innovative approach to map cosmic shear using linear algebra, statistics, and HPC.

Highlights include ML techniques for computed tomography, a scalable Gaussian process framework, safe and trustworthy AI, and autonomous multiscale simulations.

LLNL, Arizona State University and Michigan State University will dive deep into uncovering the compositions of 70 exoplanets through the Computing Grand Challenge Program, which allocates significant quantities of institutional computational resources to scientists to perform cutting-edge research.

MuyGPs helps complete and forecast the brightness data of objects viewed by Earth-based telescopes.

Testbed Environment for Space Situational Awareness software helps to track satellites and space debris and prevent collisions.