Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

Bradley Crowell with the U.S. Nuclear Regulatory Commission sees advanced materials integrity, radiological measurement, and environmental capabilities on his first visit to PNNL.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

Scientists have developed a better way to recognize denial-of-service internet attacks, improving detection by 90 percent.

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

Joint appointments at PNNL and UTEP are teaming together to strengthen and protect sensitive data.

The Forefront23 workshop convened researchers, scientists, and engineers who are just that: at the forefront of cybersecurity and nuclear nonproliferation.

Workers feeling a specific form of stress are more likely than others to become the victims of a phishing attack, according to a new study.

The papers were featured in the security and quantum sessions at the 2023 International Symposium on Computer Architecture.

O’Neil met with members of parliament, the shadow parliament, and the UK’s national security organizations. The entire board, along with 100 invited cybersecurity professionals, attended a special event at the House of Commons.

The complex interactions between the land and atmosphere can affect cloud growth.

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

Researchers have knitted together three large databases of information to help cybersecurity experts detect and prevent attacks.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.

Based on the early success of CHIRP and the urgency to build the future cybersecurity workforce, the program recently received five million dollars in funding through the FY23 Defense Appropriations Bill, via SSC.

Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.