PCA-RaNN recasts latent neural operator learning as PCA-reduced random-feature linear regression, achieving 1-3 orders faster training than standard methods on PDE benchmarks while adding conformal uncertainty quantification.
M.The Finite Element Method in Electromagnetics(John Wiley & Sons, 2015)
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Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification
PCA-RaNN recasts latent neural operator learning as PCA-reduced random-feature linear regression, achieving 1-3 orders faster training than standard methods on PDE benchmarks while adding conformal uncertainty quantification.