68 results found
Filters applied: Renewable Energy, Graph and Data Analytics, Biology, Nuclear Material Science
INITIATIVE

Molecular Observation Network

The Molecular Observation Network is a national open science network designed to produce a comprehensive database of molecular and microstructural information on soil, water, microbial communities, and biogenic emissions.

Ocean Dynamics & Modeling

PNNL's Ocean Dynamics Modeling group studies coastal processes such as marine-hydrokinetic energy, coastal circulations, storm surge and extreme waves, tsunamis, sediment transport and nutrient-macroalgal dynamics.

PNNL @ NeurIPS 2020

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.

PREPARES

PREPARES demonstrates linkages between climate or weather conditions and human domain systems by combining quantitative geophysical data with qualitative data.

Project Schedule Visualizer

The user-friendly Project Schedule Visualizer software developed at PNNL helps users readily identify and understand the impacts of updates to the schedule, budget, and risks associated with large, complex projects that cross departments.

STOMP

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.
PROGRAM

Tethys

PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.

Triton

The Triton Initiative was created to help the marine energy industry better understand the environmental impacts of marine energy devices.

Trusted and Responsible AI

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.