Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification

• URL: http://arxiv.org/abs/2606.29440v1
• Date: Sun, 28 Jun 2026 15:02:26 GMT
• Title: Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification
• Abstract summary: We introduce PCA--RaNN, a neural operator that combines a PCA-based dimensionality reduction with fixed random features.<n>It recasts operator learning as fixed regression, reducing latent regression to three orders of magnitude.
• Score: 47.720075875232226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-convex training. We introduce PCA--RaNN, a randomized latent neural operator that combines PCA-based dimensionality reduction with fixed random features and a closed-form least-squares readout. It recasts latent operator learning as fixed-feature linear regression, reducing training time by one to three orders of magnitude across benchmarks while maintaining competitive accuracy. We introduce an energy-matched scaling rule and a lightweight two-parameter BFGS refinement to correct suboptimal feature scales. Ensemble averaging reduces predictive variance. On Burgers, Darcy, Navier--Stokes and backward heat equation benchmarks, PCA--RaNN provides a favorable speed--accuracy trade-off against operator-learning baselines. The ensemble supports split-conformal prediction intervals, and the linear readout enables rapid online adaptation via recursive least squares without retraining hidden features. This provides an efficient, uncertainty-aware surrogate for many-query scientific workflows.

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