Topic: Critical Infrastructure

LLNL is applying ML to real-world applications on multiple scales. Researchers explain why water filtration, wildfires, and carbon capture are becoming more solvable thanks to groundbreaking data science methodologies on some of the world’s fastest computers.

By taking weather variables such as wildfire, flooding, wind, and sunlight that directly impact the electrical grid into consideration, researchers can improve electrical grid model projections for a more stable future.

As part of the Exascale Computing Project’s ExaSGD project, a team including LLNL researchers ran HiOp, an open source optimization solver, on 9,000 nodes of Oak Ridge National Laboratory’s Frontier exascale supercomputer.

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.

A novel ML method discovers and predicts key data about networked devices.

Collaborative autonomy software apps allow networked devices to detect, gather, identify and interpret data; defend against cyber-attacks; and continue to operate despite infiltration.

More than 100 million smart meters have been installed in the U.S. to record and communicate electric consumption, voltage, and current to consumers and grid operators. LLNL has developed GridDS to help make the most of this data.

LLNL’s cyber programs work across a broad sponsor space to develop technologies addressing sophisticated cyber threats directed at national security and civilian critical infrastructure.

Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.