The Human Factors Symposium took place at Discovery Hall at PNNL in May 2023. Fifty-seven attendees participated in the three-day event representing 15 different institutions.
Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.
At the Nonproliferation, Counterproliferation, and Disarmament Science Gordon Research Conference, researchers from PNNL shared research and scientific approaches for countering diverse threats.
PNNL-Sequim scientists will spend the next year testing a new technology that could allow the ocean to soak up more carbon dioxide without contributing to ocean acidification.
Brown University Applied Mathematics and Engineering Professor George Karniadakis has driven solutions for science and engineering problems for over ten years with a joint appointment at 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.
A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.
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.
PNNL recently joined the Department of Homeland Security for two technical meetings exploring national security research spanning the threat realm, from chemical and biological attacks to adversarial artificial intelligence.