The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

A new report details the potential for floating offshore wind to lower energy costs and enhance the resilience of the nation’s western grid.

PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

PNNL's accomplishments in FY 2024 help address the remaining challenges the wind industry faces.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

Developed with community input, the new Hydropower Vision Roadmap lays out a path to achieve a shared vison for modern hydropower by 2050.

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

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

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

DOE announced a new project, led by PNNL and National Renewable Energy Laboratory, to expand wind energy transmission off the nation’s West Coast.

Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning to protect computer networks.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

James Ang, John Feo, and Marco Minutoli organized a high performance computing workshop at the prestigious ISC High Performance 2022 conference.

A new PNNL study quantifies hydropower's contribution to grid stability. When other power sources go out, hydropower can ramp up, recoup shortfalls, and stabilize the grid nearly instantaneously.

PNNL researchers contributed to the International Conference on Data Science and Advanced Analytics 2021 Best Paper Award-winning publication.

DOE’s continued support will allow CENATE researchers to advance computing through the evaluation of novel technologies.

PNNL computer scientist Ang Li serves as one of the principal investigators for a project selected to receive DOE funding.

Researchers to design their own computer accelerators for faster analysis of large datasets.

PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.

Sriram Krishnamoorthy, a computer scientist at PNNL, collaborated with a University of Utah team on a student computing research project that won Best Student Paper at SC20.