AINeutralarXiv – CS AI · 18h ago6/10
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Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics
Researchers present two physics-constrained probabilistic frameworks (PC-SNGP and PC-SNER) for industrial prognostics that improve prediction accuracy and uncertainty quantification by maintaining awareness of input distance from training data. The methods use spectral normalization to preserve distance representations and dynamic weighting strategies, demonstrating improved performance on bearing failure prediction benchmarks while maintaining robustness under distributional shifts.