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...

Description complète

Détails bibliographiques

Auteurs principaux:	Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua
Format:	Artigo
Langue:	Inglês
Publié:	Wiley 2026-06-01
Collection:	Advanced Intelligent Systems
Sujets:	Bayesian neural networks insulation physics informed neural networks prognostics & health management uncertainty quantification
Accès en ligne:	https://doi.org/10.1002/aisy.202501502
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!