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Researcher
- Ryan Dehoff
- Singanallur Venkatakrishnan
- Yong Chae Lim
- Amir K Ziabari
- Diana E Hun
- Philip Bingham
- Philip Boudreaux
- Rangasayee Kannan
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- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Srikanth Yoginath
- Sudarsanam Babu
- Tomas Grejtak
- Varisara Tansakul
- William Peter
- Yarom Polsky
- Yiyu Wang
- Yukinori Yamamoto
- Zhili Feng

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).

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

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

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

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches.

The technologies provide a coating method to produce corrosion resistant and electrically conductive coating layer on metallic bipolar plates for hydrogen fuel cell and hydrogen electrolyzer applications.

Welding high temperature and/or high strength materials for aerospace or automobile manufacturing is challenging.