PNNL’s data-infused approach to electron microscopes’ use in scientific experimentation will help researchers and industry interpret large data streams and drive down costs.
PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.
The first customized resource of its kind, H-BEST analyzes the indoor environmental quality profile for buildings and helps its users identify the costs and benefits of improvements.
Svitlana Volkova, chief scientist for decision intelligence and analytics at PNNL, was invited as a panelist at the SIAM International Conference on Data Mining
National Nuclear Security Administration Graduate Fellow Marc Wonders has spent the past year working with researchers exploring artificial intelligence in the national security mission space.
PNNL’s Supriya Goel has been named by Consulting-Specifying Engineer as one of 2021’s 40 outstanding nonresidential building industry professionals age 40 or younger.
When the COVID-19 pandemic halted all travel for in-person inspections, a team at PNNL knew they needed to find a way to perform assessments virtually. Their solution—a portable kit that could be shipped to locations.
A webapp developed by PNNL in collaboration with the University of Washington to help drive efficiencies for urban delivery drivers is now in the prototype stage and ready for testing.
PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.
Vigorous and rapid air exchanges might not always be a good thing when it comes to levels of coronavirus particles in a multiroom building, according to a new modeling study.