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 computer scientists are presenting the latest research in HPC at SC20.

RadAnalysis is a software developed by the Department of Energy to assist in the packaging and transportation of radioactive materials. It facilitates compliance with regulations, providing consistency, accuracy, and relevant documentation.

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’s Space Science, Technology, and Policy activities draw on diverse technical expertise and research capabilities from across the Laboratory.

A software suite for working with neutron activation rates measured in a nuclear fission reactor, an accelerator-based neutron source, or any neutron field to determine the neutron flux spectrum using a generalized least-squares approach.

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.