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%2C a novel way of implementing neural operators%2C i.e.%2C to learn the smooth dependence of solutions on geometric deformations. Specifically%2C given a reference solution%2C RNO can predict solutions corresponding to arbitrary deformations of the referred geometry. This approach turns out to be much more data efficient. Through extensive experiments%2C we show that RNO can learn the dependence across various types and different numbers of geometry objects with relatively small datasets. RNO outperforms baseline models in accuracy by a large lead and achieves up to 80%25 error reduction.?quality=80&w=800)
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