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.
PREPARES demonstrates linkages between climate or weather conditions and human domain systems by combining quantitative geophysical data with qualitative data.
PNNL combines AI and cloud computing with damage assessment tools to predict the path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
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.
STOMP is a suite of numerical simulators for solving problems involving coupled flow and transport processes in the subsurface. The suite of STOMP simulators is distinguished by application areas and solved mathematical equations.
PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.
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.
The UNSAT-H computer code is used to understand the movement of water, heat, and vapor in soils so more informed decisions can be made about land use, waste disposal, and climate change.
The Water Cycle and Climate Extremes Modeling (WACCEM) Scientific Focus Area advances predictive understanding of water cycle variability and change through foundational research using models, observations, and novel numerical experiments.
