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News/Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification
arxiv
PublishedJune 30, 2026 at 4:00 AM
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Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification

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Publisher summary· verbatim

arXiv:2606.29440v1 Announce Type: new 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 operat

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    PCA--RaNN
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#machine-learning#numerical-analysis#optimization#uncertainty-quantification

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Mentioned models
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  • 01
    PCA--RaNN
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arxiv
Read original ↗All from arxiv →
Tags
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#machine-learning#numerical-analysis#optimization#uncertainty-quantification

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