PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.

The COMPASS project aims to increase fundamental understanding, models, and the ability to predict change in coastal systems.

The Data-Model Convergence (DMC) Initiative is a multidisciplinary effort to create the next generation of scientific computing capability through a software and hardware co-design methodology.

PNNL deploys advanced drone systems to tackle the nation’s most pressing challenges in energy, environmental remediation, and national security.

PNNL is a testbed for the latest research and technologies in marine carbon dioxide removal (mCDR)—leveraging the ocean’s strength as a natural carbon sink to address pressing climate concerns.

PNNL's Ocean Dynamics Modeling group studies coastal processes such as marine-hydrokinetic energy, coastal circulations, storm surge and extreme waves, tsunamis, sediment transport and nutrient-macroalgal dynamics.

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 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.

The Salish Sea Model (SSM) is a predictive coastal ocean model for estuarine research, restoration planning, water-quality management, and climate change response assessment.

The SEAHORCE project aims to improve the ability of the Energy Exascale Earth System Model to model oceans at higher fidelity than currently possible.

PNNL creates immersive software experiences to meet a variety of challenges. One such challenge in science, technology, engineering, and mathematics (STEM) education is providing quality computer science education for all students.