Neural flow operators with composition and separation structures are proven to universally approximate any operator in finite and infinite dimensions, recovering ResNet-type and plain architectures via time discretizations.
Proceedings of the 40th International Conference on Machine Learning , series=
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Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approcimations
Neural flow operators with composition and separation structures are proven to universally approximate any operator in finite and infinite dimensions, recovering ResNet-type and plain architectures via time discretizations.