#### Topological Analysis

LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.

#### NSDE

The NSDE project is focused on research and development of nonlinear solvers and sensitivity analysis techniques for nonlinear, time-dependent, and steady-state partial differential equations.

#### PSUADE

The flourishing of simulation-based scientific discovery has also resulted in the emergence of the UQ discipline, which is essential for validating and verifying computer models.

#### 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.