New B-PINN framework enhances uncertainty quantification for material degradation prognostics

Researchers have developed a new Bayesian Physics-Informed Neural Network (B-PINN) framework designed to improve uncertainty quantification in prognostics and health management (PHM). This novel approach jointly models both epistemic and aleatoric uncertainty, offering more comprehensive predictive posteriors for applications like estimating insulation material ageing. The framework was evaluated on transformer insulation ageing, validated with thermal models and field measurements, and demonstrated superior accuracy and calibration compared to existing deterministic and Bayesian PINN variants. AI

IMPACT This research could lead to more reliable risk assessment and decision-making in critical infrastructure asset management.

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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• Bayesian Neural Networks
• Physics-Informed Neural Networks
• transformer insulation ageing

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