#### GOLLNLP

Responding to a DOE grid optimization challenge, an LLNL-led team developed the mathematical, computational, and software components needed to solve problems of the real-world power grid.

#### GEFIE-QUAD

This first-principles simulation method models the interaction of laser light with diffraction gratings, giving scientists a powerful tool to predict the performance of a laser compressor.

#### ExReDi

The Extreme Resilient Discretization project (ExReDi) was established to address these challenges for algorithms common for fluid and plasma simulations.

#### LLNL uses ML to derive black hole motion from gravitational waves

An LLNL mathematician and collaborators have developed a machine learning–based technique capable of deriving a mathematical model for the motion of binary black holes from gravitational wave data.

#### Using supercomputers to optimize hot rolling for the steels of tomorrow

In a project with U.S. Steel, LLNL computational physicists built models of the hot-rolling process to run on LLNL’s HPC platforms.

#### Inaugural industry forum inspires ML community

LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12, co-hosted by the Lab’s High-Performance Computing Innovation Center and Data Science Institute.