Filtered by Computational Research, Hydropower, Hydropower and the Electric Grid, Radiation Measurement, Science of Interfaces, Solar Energy, Waste Processing, and Water Power
PNNL is leading the nation with research addressing urgent needs for reimagining U.S. critical infrastructure against the realities of software-speed attacks and hazards.
PNNL and ORNL are working together on Digital Twins to modernize the U.S. hydropower plant fleet, which will reduce operating costs, improve reliability, reduce downtime, enhance grid resiliency, and reduce environmental impacts.
The Interfacial Dynamics in Radioactive Environments and Materials (IDREAM) Energy Frontier Research Center (EFRC) conducts fundamental science to support innovations in retrieving and processing high-level radioactive waste.
By improving the Weather Research and Forecasting (WRF)-Solar model, this project aims to reduce forecast errors, improve sub-grid scale variability estimates, and more accurately estimate forecast uncertainty.
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.
PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.
A software suite for working with neutron activation rates measured in a nuclear fission reactor, an accelerator-based neutron source, or any neutron field to determine the neutron flux spectrum using a generalized least-squares approach.
