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Learnable composition for neural operators

This story is from 2026-09-04. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.03069v1 Announce Type: new Abstract: Neural operators are fast, differentiable surrogates for physical simulation, but their accuracy often degrades when domain geometry, size, or operating conditions differ from training. Supervised adaptation can recover accuracy, but even a small targ…

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  1. 2026-09-04 04:00 · arXiv cs.LG
    Learnable composition for neural operators

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