Optimization and Machine Learning for Safeguarding Cyber-Physical Systems
New mathematical tools developed at PNNL hold promise to transform the way we operate and defend complex cyber-physical systems, such as the power grid.
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.
AI for Energy Report Features PNNL Expertise
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
The Impact of Pruning
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
Solving Scientific Problems with Institutional Collaborations
Brown University Applied Mathematics and Engineering Professor George Karniadakis has driven solutions for science and engineering problems for over ten years with a joint appointment at PNNL.
Affordable, Reliable and Efficient-Energy Research is PNNL Focus at ARPA-E Energy Innovation Summit
Regional Science Bowl Tests Knowledge and Adaptability
High school students from across Washington State competed in the Pacific Northwest Regional Science Bowl, hosted online by PNNL, for a chance to advance to the national competition in May.