Understanding and Predicting Extreme Rainfall Events through Machine Learning
Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.
Connecting River Flows and El Niño with AI
Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.
Delivering Mighty Impact for DOE User Facility
ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.
PNNL Joins Science Leaders on National Stage in Seattle
PNNL researchers and professional staff led discussions ranging from biothreats and climate change to science careers at the 2020 annual meeting of the American Association for the Advancement of Science, held this year in Seattle.
Integrating Domain Science Data with Artificial Intelligence
Neeraj Kumar discusses how AI can transform scientific research at the Platform for Advanced Scientific Computing Conference and Trillion Parameter Consortium European Workshop.
Physics-Informed Machine Learning for Energy and Environment
Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.
Disaster Response and Mitigation in an AI World
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.
Artificial Intelligence Brings Better Hurricane Predictions
A new model offers more accurate predictions of how intensely hurricanes may strike.