Red teaming for CPS, the process of challenging systems, involves a group of cybersecurity experts to emulate end-to-end cyberattacks following a set of realistic tactics, techniques, and procedures.
PNNL computational biologists, structural biologists, and analytical chemists are using their expertise to safely accelerate the design step of the COVID-19 drug discovery process.
Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.
Pacific Northwest National Laboratory researchers developed a graphical processing unit (GPU)-centered quantum computer simulator that can be 10 times faster than any other quantum computer simulator.
Researchers at PNNL have developed a bacteria testing system called OmniScreen that combines biological and synthetic chemistry with machine learning to hunt down pathogens before they strike.
PNNL’s new Smart Power Grid Simulator, or Smart-PGSim, combines high-performance computing and artificial intelligence to optimize power grid simulations without sacrificing accuracy.
Infusing data science and artificial intelligence into electron microscopy could advance energy storage, quantum information science, and materials design.
Tracking down nefarious users is just one example of work at PNNL’s Center for Advanced Technology Evaluation, a computing proving ground supported by DOE’s Advanced Scientific Computing Research program.
Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.
PNNL researchers used machine learning to develop a tool for a nonprofit to identify orthopedic implants in X-ray images to improve surgical speed and accuracy.
The PNNL-developed VOLTTRON™ software platform’s advancement has benefited from a community-driven approach. The technology has been used in buildings nationwide, including most recently on a university campus.
PNNL scientists have created a tool called WatchOwl to collect more than 4 million tweets per day related to the COVID-19 pandemic. The tool analyzes tweets related to interventions like social distancing and movement restrictions.