PNNL combines AI and cloud computing with damage assessment tools to predict the path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
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.
STOMP is a suite of numerical simulators for solving problems involving coupled flow and transport processes in the subsurface. The suite of STOMP simulators is distinguished by application areas and solved mathematical equations.
PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.
This 18-month study will analyze how the region can meet its needs for reliable, resilient, and affordable energy along with decarbonization goals and other energy policies and priorities.
Triton aims to reduce barriers to deployment of marine energy devices through research and advancement of environmental monitoring tools and methodologies.
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.
PNNL creates immersive software experiences to meet a variety of challenges. One such challenge in science, technology, engineering, and mathematics (STEM) education is providing quality computer science education for all students.
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.
