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arXiv Machine Learning By Louis Serrano, Jiequn Han, Edouard Oyallon, Shirley Ho, Rudy Morel

Test-time Generalization for Physics through Neural Operator Splitting

Read the original on arXiv Machine Learning →

arXiv:2602. 00884v2 Announce Type: replace Abstract: Neural operators have shown promise in learning solution maps of partial differential equations (PDEs), but they often struggle to generalize when test inputs lie outside the training distribution, such as novel initial conditions, unseen PDE coefficients or unseen physics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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