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 partners with agencies and industry to identify and engage historically disadvantaged populations in regulatory decision-making, environmental assessment, and impact estimation of the consequences of complex polices and projects.

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.

PNNL designs, delivers, and manages training programs that enable partners worldwide to understand their individual or organizational roles and responsibilities, fulfill a job function, or strengthen a particular skill set.

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.

PNNL is working on behalf of the U.S. Department of Energy to create a prototype system that enables homes to help provide services to the power grid while delivering economic benefits to residents.

PNNL develops training, exercises, and assessments to prepare and equip border security officers to detect, identify, and interdict the illicit movements of materials, commodities, and components associated with WMD.