Energy experts at PNNL are helping 56 states and territories deliver their state energy security plans (SESPs) through a transparent and collaborative review process.
Pacific Northwest National Laboratory is partnering with the U.S. Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency to develop and operate the Control Environment Laboratory Resource
In January 2024, CESER—in partnership with GDO, NASEO, and PNNL—created a set of state energy security cohorts to support the coordination and technical development of state energy security planning, assessment, and mitigation.
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.
The RD2C laboratory-directed research initiative seeks to develop resilient, adaptive, and intelligent sensing and control algorithms through the observational understanding and characterization of CPSs under adverse conditions.
