PNNL is leading the nation with research addressing urgent needs for reimagining U.S. critical infrastructure against the realities of software-speed attacks and hazards.
Cyber, physical, and blended cyber-physical threats are real, ubiquitous, and expensive to deal with. Private companies, government institutions, and critical infrastructures struggle to implement viable solutions as technology evolves.
The CVAir pilot project transports computed tomography passenger baggage data from originating international airports for targeted flights destined for the U.S.
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.
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.
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.
IrrigationViz is a visual decision-support tool that provides users with high-level estimates for irrigation modernization projects, such as concrete lining for a canal or replacing a canal with a pipeline.
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 is a testbed for the latest research and technologies in marine carbon dioxide removal (mCDR)—leveraging the ocean’s strength as a natural carbon sink to address pressing climate concerns.
Advancing the understanding and monitoring of nuclear material processing to accelerate development and qualification of new material systems for national security and nuclear energy.
Mega AI seeks to develop massive-scale, self-supervised, multimodal foundation models of scientific knowledge capable of general-purpose inferences to enable reasoning with existing knowledge and discovery of new knowledge.
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.
