Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
arXiv:2601.03673v2 Announce Type: replace-cross Abstract: 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 are deterministic or account only for epistemic uncertainty, limiting their suitability for risk-aware decision-making. This work introduces a heteroscedastic Bayesian Physics-Informed Neural Network (B-PINN) framework that jointly models ep
The increasing complexity of industrial systems and the limitations of deterministic models necessitate advanced uncertainty quantification in AI applications for predictive maintenance.
Improved uncertainty quantification in AI-driven prognostics allows for more reliable condition monitoring and risk-aware decision-making in critical infrastructure and manufacturing.
This work introduces a framework that can distinguish between inherent system randomness and model uncertainty in physics-informed neural networks, leading to more robust predictive models.
- · Industrial operators
- · Maintenance solutions providers
- · High-value asset manufacturers
- · Safety-critical industries
- · Providers of purely deterministic predictive maintenance solutions
- · Organizations with high tolerance for unexpected failures
More accurate and reliable predictions of material degradation and system failure become possible.
Optimized maintenance schedules and reduced unplanned downtime lead to significant operational cost savings and increased asset longevity.
The enhanced trustworthiness of AI in PHM could accelerate the adoption of autonomous maintenance systems across industries.
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Read at arXiv cs.AI