Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (23)
- Computing and Computational Sciences Directorate (35)
- Energy Science and Technology Directorate (217)
- Fusion and Fission Energy and Science Directorate (21)
- Isotope Science and Enrichment Directorate (6)
- National Security Sciences Directorate (17)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (128)
- User Facilities (27)
- (-) Information Technology Services Directorate (2)
Researcher
- Vincent Paquit
- Ryan Dehoff
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Diana E Hun
- Philip Bingham
- Philip Boudreaux
- Stephen M Killough
- Akash Jag Prasad
- Bryan Maldonado Puente
- Calen Kimmell
- Canhai Lai
- Chris Tyler
- Clay Leach
- Corey Cooke
- Costas Tsouris
- Gina Accawi
- Gurneesh Jatana
- James Haley
- James Parks II
- Jason Jarnagin
- Jaydeep Karandikar
- Kevin Spakes
- Lilian V Swann
- Mark M Root
- Mark Provo II
- Michael Kirka
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Rob Root
- Ryan Kerekes
- Sally Ghanem
- Sam Hollifield
- Vladimir Orlyanchik
- Zackary Snow

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

System and method for part porosity monitoring of additively manufactured components using machining
In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

Sensing of additive manufacturing processes promises to facilitate detailed quality inspection at scales that have seldom been seen in traditional manufacturing processes.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).

Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.