The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.
By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.
Researchers at PNNL examined heat pump water heater (HPWH) operation in Pacific Northwest residences, gaining insights into HPWH electricity use patterns. Part of the study captured trends during a COVID-19 stay-at-home order.
Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.
PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.