Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics
arXiv:2512.08499v3 Announce Type: replace Abstract: Development of reliable and physically interpretable probabilistic frameworks for industrial prognostics remain nascent, and existing literature is often insensitive as inputs move away from the training manifold. In this paper, we develop two sampling-free, distance-aware physics-constrained probabilistic frameworks: (i) PC-SNGP and (ii) PC-SNER. Both apply spectral normalization to hidden layer weights, enforcing bi-Lipschitz distance-preserving representation from the input to the latent space. PC-SNGP replaces the dense output with Gaussi
The accelerating complexity and criticality of industrial systems necessitate more robust and interpretable AI for prognostics, moving beyond basic correlative models.
Reliable, physics-constrained AI frameworks enhance the safety, efficiency, and predictability of industrial operations by providing distance-aware uncertainty quantification.
The ability to develop more trustworthy and physically consistent AI for industrial asset management and failure prediction through interpretable probabilistic models.
- · Industrial operators
- · AI/ML researchers
- · Predictive maintenance software vendors
- · Critical infrastructure sectors
- · Companies relying on black-box AI
- · Systems with high failure rates
- · Traditional statistical prognostics
Improved reliability and reduced downtime in complex industrial systems using AI for prognostics.
Increased adoption of physics-informed AI across various engineering disciplines, driving innovation in system design and operations.
Elevated regulatory scrutiny and standardization efforts for AI deployed in safety-critical industrial applications due to enhanced interpretability.
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