Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities. Most existing PINN‐based prognostics approaches...

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Главные авторы:	Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Wiley 2026-06-01
Серии:	Advanced Intelligent Systems
Предметы:	Bayesian neural networks insulation physics informed neural networks prognostics & health management uncertainty quantification
Online-ссылка:	https://doi.org/10.1002/aisy.202501502
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