Artificial intelligence (AI) is increasingly used to analyze medical images, material data, and scientific measurements, but many systems suffer when real-world data does not match ideal conditions. Measurements collected from different instruments, experiments, and simulations often vary widely in resolution, noise, and reliability. Traditional machine learning models typically assume that these differences are negligible, and this assumption can limit their accuracy and reliability.
To address this issue, researchers at Penn State University have developed a new artificial intelligence framework with potential impact on fields ranging from Alzheimer's disease research to advanced materials design. This approach, called ZENN, is detailed in research showcased in the Proceedings of the National Academy of Sciences and teaches AI models to recognize and adapt to hidden differences in data quality rather than ignoring them.
ZENN (short for Zentropy-Embedded Neural Networks) was developed by Shun Wang, a postdoctoral researcher in materials science and engineering. Wenrui Hao (Professor of Mathematics), Zi-Kui Liu Professor of Materials Science and Engineering, and Shunli Shang Research Professor (Professor of Materials Science and Engineering).
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