#### Alkemi

Simulation workflows for ALE methods often require a manual tuning process. We are developing novel predictive analytics for simulations and an infrastructure for integration of analytics.

#### HYPRE

The *hypre* library's comprehensive suite of scalable parallel linear solvers makes large-scale scientific simulations possible by solving problems faster.

#### 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.

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#### 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.

#### Conference papers highlight importance of data security to machine learning

The 2021 Conference on Computer Vision and Pattern Recognition features two papers co-authored by an LLNL researcher targeted at understanding robust machine learning models.