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.
From global issues such as melting permafrost and the creation of alternate biofuels to matters affecting microbiomes and micro-sized life, PNNL research is featured in news publications worldwide.
The U.S. Department of Energy-sponsored Internet of Things Upgradeable Lighting Challenge is designed to encourage the widespread adoption of IoT-Upgraded Lighting.
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.
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's Subsurface Biogeochemical Research Scientific Focus Area works to transform understanding of spatial and temporal dynamics in river corridor hydrobiogeochemical functions from molecular reaction to watershed and basin scales.
The Salish Sea Model (SSM) is a predictive coastal ocean model for estuarine research, restoration planning, water-quality management, and climate change response assessment.
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 develops training, exercises, and assessments to prepare and equip border security officers to detect, identify, and interdict the illicit movements of materials, commodities, and components associated with WMD.
