PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.
Researchers use models to represent relationships between climate and socio-economic processes, helping inform decisions for slowing climate change and enhancing resilience.
The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.
The roles of the various environmental variables in the transition from suppressed to active tropical precipitation regimes are characterized using statistical analysis and machine learning.
This study revealed that fresh organic vapors are soluble in particulate organics that are actively growing in size. However, if the particulate matter ages, fresh organic vapors can no longer mix with the organic matter.