Idaho National Laboratory
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- Idaho, US
- https://inl.gov
- cody.permann@inl.gov
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Repositories
- repository-statistics Public
Tracking repository statistics over time for projects on GitHub under IdahoLab, IdahoLabResearch and IdahoLabUnsupported.
idaholab/repository-statistics’s past year of commit activity - DACKAR Public
DACKAR is a software product designed to analyze equipment reliability data and provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind, and to predict their direct consequences.
idaholab/DACKAR’s past year of commit activity - ResDEEDS Public
The Resilience Development for Electric Energy Delivery Systems (ResDEEDS) tool walks users through the process of evaluating electric energy delivery systems (EEDS) for resiliency. It implements the steps of the INL Resilience Framework for EEDS and provides automated tracking of resilience planning and suggestions for mitigating hazards.
idaholab/ResDEEDS’s past year of commit activity - DOVE Public
The Dispatch Optimization Variable Engine (DOVE) is software tool written in python, developed at Idaho National Laboratory (INL) that provides an easily accessible application-programming-interface (API) to performing resource dispatch optimization analysis for integrated energy system (IES) configurations.
idaholab/DOVE’s past year of commit activity - swift Public
The code adds a spectral solver capability to the MOOSE ecosystem. It operates on regular orthogonal grids, often used for representative volume elements. This new solver can operate on graphics processing units (GPUs) and can couple to existing MOOSE models.
idaholab/swift’s past year of commit activity - raven Public
RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.
idaholab/raven’s past year of commit activity - PANDA Public
This software provides dislocation-type defect identification and segmentation using a standard open source computer vision model, YOLO11, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of expert annotated micrographs for training.
idaholab/PANDA’s past year of commit activity