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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.
Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.
Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modelling building height at scale are hindered by two pervasive issues.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.
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.