PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

PNNL is honoring its postdoctoral researchers as part of the fourteenth annual National Postdoc Appreciation Week with seven profiles of postdocs from around the Laboratory.

PNNL celebrates World Energy Storage Day (September 22) by introducing you to some of our best and brightest in this area.

Stinis will serve as a principal investigator on two of the four projects selected for funding by the Advanced Scientific Computing Research program.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

From Mexico and Ethiopia to a U.S. national lab, interns are ready to contribute to a sustainable energy future.

PNNL’s Grid Storage Launchpad delivers tomorrow’s energy storage solutions today.

Regional wind data helps utilities save money, plan for windy days.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.

A holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.

Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

Four PNNL researchers received highly competitive DOE Early Career Research Program awards, providing five continuous years of funding for their projects.

Zhijie (Jay) Xu will apply his expertise in multiscale modeling to this editorial role.

A new open-source feature tracking package is now available to facilitate advanced model evaluation, model development efforts, and scientific discovery.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

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