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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Principais autores:	Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Wiley 2026-06-01
coleção:	Advanced Intelligent Systems
Assuntos:	Bayesian neural networks insulation physics informed neural networks prognostics & health management uncertainty quantification
Acesso em linha:	https://doi.org/10.1002/aisy.202501502
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