PNNL partnered with the Treasury and AWS to develop Cache, a cloud-based tool that allows the Treasury’s disparate data to be easily searched, translated, extracted, linked, and analyzed.
The Center for AI @PNNL is driving a research agenda that explores the foundations and emerging frontiers of AI, combining capability development and application to mission areas in science, security and energy resilience.
The Center for Understanding Subsurface Signals and Permeability (CUSSP) Energy Earthshot Research Center (EERC) is working to develop the ability to predict and control fluid flow through fracture networks in enhanced geothermal systems.
The CVAir pilot project transports computed tomography passenger baggage data from originating international airports for targeted flights destined for the U.S.
PNNL and ORNL are working together on Digital Twins to modernize the U.S. hydropower plant fleet, which will reduce operating costs, improve reliability, reduce downtime, enhance grid resiliency, and reduce environmental impacts.
E4D is a 3D geophysical modeling and inversion program designed for subsurface imaging and monitoring using static and time-lapse electrical resistivity tomography (ERT), spectral induced polarization (SIP) and travel-time tomography data.
PNNL is a leader in the integration of aberration-corrected electron microscopy, in-situ techniques, and atom probe tomography to address challenges in nuclear materials, environmental remediation, energy storage, and national security.
GeoBOSS is a software library that combines the data-handling capabilities of Spark and the user-friendliness of Python to simplify geospatial analytics and the transition between small-scale research and large-scale operational projects.
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 is heavily engaged in the development and use of mass spectrometry technology across its science, energy, and security missions, from fundamental research through mature operational capabilities.
PNNL wind energy experts are helping to design a new avian radar system that will be equipped on lidar buoys to detect avian activity over open water and near offshore wind turbines.
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.