The RD2C laboratory-directed research initiative seeks to develop resilient, adaptive, and intelligent sensing and control algorithms through the observational understanding and characterization of CPSs under adverse conditions.
Powered by few-shot learning, the Sharkzor AI-driven, scalable web application makes it possible to quickly characterize and sort electron microscopy images used to analyze radioactive materials.
PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.
PNNL has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.
UTEP and PNNL are advancing the collective scientific impact of both institutions through collaborations between PNNL researchers and UTEP faculty, as well as by building on the complementary strengths to grow a diverse STEM workforce.
Visual Sample Plan (VSP) is a software tool that supports the development of a defensible sampling plan based on statistical sampling theory and the statistical analysis of sample results to support confident decision making.
The Water cycle: Modeling of Circulation, Convection, and Earth system Mechanisms (WACCEM) Scientific Focus Area advances predictive understanding of water cycle variability and change.
PNNL wind energy experts led a project to review existing literature focusing on the technical evaluation of offshore wind energy transmission through potential points of interconnection at the West Coast.
Pacific Northwest National Laboratory and National Renewable Energy Laboratory conducted a two-year study to investigate the potential of floating offshore wind to help meet growing energy needs on the U.S. West Coast.
