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.
The E-COMP Initiative is creating new capabilities that enable the optimized design and operation of energy systems subject to multiple objectives and with high levels of power electronic (PEL) driven devices.
PNNL is laying the groundwork for advancing energy equity and environmental justice through research to develop an innovative energy system that benefits everyone
PNNL and collaborators have established a national heat pump and heat pump water heater partnership to help drive adoption of these energy-saving technologies in both residential and commercial buildings.
PNNL administers two research buoys for the U.S. Department of Energy that allows collection of wind meteorological and oceanographic data off the nation's coasts.
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.
The Pacific Northwest National Laboratory is developing a Port Electrification Handbook—a reference to aid maritime ports nationwide in their clean energy transition.
The user-friendly Project Schedule Visualizer software developed at PNNL helps users readily identify and understand the impacts of updates to the schedule, budget, and risks associated with large, complex projects that cross departments.
Poorly insulated walls in residential buildings waste an estimated quadrillion+ Btus of energy per year. Upgrading windows and insulation during re-siding projects is a unique, cost-effective opportunity to improve efficiency and comfort.
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 has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.
Visual Sample Plan (VSP) is a software tool that supports the development of a defensible sampling plan based on statistical sampling theory and the statistical analysis of sample results to support confident decision making.
