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The aim is to develop neural architectures that effectively map between infinite-dimensional function spaces for operator learning.

Principled approaches for extending neural architectures to function spaces for operator learning

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The aim is to develop neural architectures that effectively map between infinite-dimensional function spaces for operator learning.
Identified key principles for constructing neural architectures for function spaces.
Proposed a recipe to convert popular neural networks into neural operators.
Outlined practical steps to enhance the effectiveness of these models.
Neural operators effectively extend conventional neural networks to infinite-dimensional problems.
Architectural refinements lead to improved performance in operator learning tasks.
The proposed method shows a systematic approach for bridging finite-dimensional networks to scientific applications.

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Randomized trial shows effective neural operator design for scientific applications, suggesting broad impact.

Berner et al. (2026) studied this question.

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