A research buoy managed by PNNL has been deployed in Hawai’ian waters, collecting oceanographic and meteorological measurements off the coast of O’ahu.
The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.
Staff at PNNL recently completed a report highlighting commercial products enabled through projects funded by the Department of Energy’s Building Technologies Office.
PNNL mathematician Aaron Luttman contributed to the organizing committee for workshop exploring robust machine learning and artificial intelligence systems for the U.S. Army.
PNNL wind energy program manager Alicia Mahon was a guest editor in a special issue about advancements in buoy technology in the Marine Technology Society Journal.
The Simple Building Calculator, developed at PNNL, meets a need for a quick, interactive, and economic method to evaluate energy use—and potential savings from efficiency measures—in simple commercial buildings.
For a second year in a row, doctoral intern Jack Watson was awarded the Student Merit Award by the Society for Risk Analysis and the Resilience Analysis Specialty group.
A multi-institutional team of wind energy experts led by PNNL assessed the scientific grand challenges for offshore wind and provided recommendations for closing gaps in models.
Steven Spurgeon’s research is featured in the cover of the MRS Bulletin along with his team’s invited perspective on the future of machine learning for electron and scanning probe microscopy.
Five staff members from PNNL received awards from the Department of Energy’s Federal Energy Management Program for contributions to projects for the U.S. Army.
As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.
An evaluation of models and prediction tools for distributed wind turbines has unearthed data that can help potential users make the most informed decisions on upfront investments.