Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.
For her most recent efforts, Bruckner-Lea, a senior technical advisor at PNNL, received the Secretary’s Appreciation Award from the U.S. Secretary of Energy Jennifer Granholm in July.
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.
A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.
The PNNL-managed Building America Solution Center translates research into actionable considerations for homeowners and builders to provide two solutions in one: increasing energy efficiency while also enhancing disaster resistance.
Four PNNL researchers received highly competitive DOE Early Career Research Program awards, providing five continuous years of funding for their projects.
A new open-source feature tracking package is now available to facilitate advanced model evaluation, model development efforts, and scientific discovery.
This PNNL project was the focus of Nune’s talk when he delivered the keynote for the Carbon Capture and Utilization track at the 2nd Annual Baker Hughes Energy Frontiers Summit.