Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning
📰 ArXiv cs.AI
Learn to quantify uncertainty in neural operator predictions with geometry-aware post-hoc methods, enhancing reliability in tasks like PDE solving
Action Steps
- Apply REEF-GP to existing neural operator models to quantify uncertainty
- Use Gaussian Processes to model uncertainty in embedded feature spaces
- Evaluate the performance of REEF-GP on tasks with geometric variability
- Compare REEF-GP with existing uncertainty quantification methods
- Integrate REEF-GP into operator learning pipelines for improved reliability
Who Needs to Know This
Researchers and engineers working with neural operators for PDEs and tasks requiring uncertainty quantification can benefit from this approach, as it enhances the reliability of their predictions
Key Insight
💡 Geometry-aware representations learned by neural operators can be leveraged for post-hoc uncertainty quantification, improving prediction reliability
Share This
🚀 Enhance neural operator reliability with geometry-aware post-hoc uncertainty quantification using REEF-GP! 📊
Key Takeaways
Learn to quantify uncertainty in neural operator predictions with geometry-aware post-hoc methods, enhancing reliability in tasks like PDE solving
Full Article
Title: Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning
Abstract:
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
Abstract:
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
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