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)
- Information Technology Services Directorate (2)
- Isotope Science and Enrichment Directorate (6)
- National Security Sciences Directorate (17)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (128)
- User Facilities (27)
Researcher
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Philip Bingham
- Ryan Dehoff
- Vincent Paquit
- Vlastimil Kunc
- Ahmed Hassen
- Dan Coughlin
- Daniel Jacobson
- Diana E Hun
- Gina Accawi
- Gurneesh Jatana
- Jim Tobin
- Josh Crabtree
- Kim Sitzlar
- Mark M Root
- Merlin Theodore
- Michael Kirka
- Obaid Rahman
- Philip Boudreaux
- Steven Guzorek
- Subhabrata Saha
- Vipin Kumar

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

Mechanism-Based Biological Inference via Multiplex Networks, AI Agents and Cross-Species Translation
This invention provides a platform that uses AI agents and biological networks to uncover and interpret disease-relevant biological mechanisms.

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

Through the use of splicing methods, joining two different fiber types in the tow stage of the process enables great benefits to the strength of the material change.

Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.