AI Research
Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review
Bridging the gap between data-driven models and physical principles for industrial maintenance.
In modern industry, keeping complex systems reliable, safe, and efficient hinges on Prognostics and Health Management (PHM). Machine Learning (ML) has largely driven advancements in diagnostics and prognostics, yet purely data-driven models face inherent limitations, such as poor...
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ArXiv CS.LG
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ArXiv CS.LG
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Physics-Informed Machine Learning bridges the gap between raw data and physical reality to improve the reliability of industrial predictive maintenance. By embedding domain knowledge into algorithms, this approach overcomes the common black-box limitations of standard neural networks in safety-critical environments.
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