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ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.
We will develop an AI-powered autonomous software development pipeline to help urban scientists develop advanced research software (e.g., digital twins and cyberinfrastructure) to support smart city research and management without the need to write codes or know software engin
Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.