arXiv Machine Learning June 30, 2026 By Zirui Deng, Jingbo Sun, Deyu Meng, Fei Wang

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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