arXiv:2606.17513v1 Announce Type: cross Abstract: Neural operators provide fast surrogates for PDEs but their deterministic predictions limit their use in tasks requiring uncertainty quantification (UQ), especially under geometric variability. Existing approaches primarily model uncertainty in network parameters, largely overlooking the geometry-aware representations learned by the operator itself. We propose REEF-GP (Residual on Embedded Features Gaussian Process), a post-hoc UQ framework that fits a GP to the residuals of a frozen neural operator whose internal embeddings define the kernel f
The increasing complexity and deployment of neural operators require robust uncertainty quantification, particularly as AI applications move from deterministic outcomes to situations demanding risk assessment, necessitating new methods for reliability.
This development enhances the trustworthiness and applicability of neural operators in critical fields by providing a post-hoc method to quantify prediction uncertainty, especially in scenarios with geometric variability.
Neural operators, previously limited by deterministic predictions, can now be augmented with a framework (REEF-GP) to assess the reliability of their outputs, expanding their utility in scientific and engineering tasks requiring confidence measures.
- · AI/ML researchers
- · Engineering simulation
- · Scientific computing
- · Critical infrastructure relying on AI
- · Deterministic simulation approaches
- · Methods requiring costly retraining for UQ
Improved reliability and broader adoption of AI-driven PDE solvers across various industries.
Reduced need for expensive physical experiments or complex traditional simulations due to more trustworthy AI surrogates.
Acceleration of design cycles and discovery in fields like material science or drug discovery, powered by more credible AI models.
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Read at arXiv cs.AI