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 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.
PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.
Developed by PNNL, the Threat Risk and Event Analysis, Detection, and Surveillance framework offers an adaptable capability to meet new and changing threats compromising human, animal, plant, and environmental health.
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 Water cycle: Modeling of Circulation, Convection, and Earth system Mechanisms (WACCEM) Scientific Focus Area advances predictive understanding of water cycle variability and change.
PNNL wind energy experts led a project to review existing literature focusing on the technical evaluation of offshore wind energy transmission through potential points of interconnection at the West Coast.
Pacific Northwest National Laboratory and National Renewable Energy Laboratory conducted a two-year study to investigate the potential of floating offshore wind to help meet growing energy needs on the U.S. West Coast.
