What is recorded here?
Combines a branch network encoding sampled input functions with a trunk network encoding output locations, taking their learned feature inner product to approximate nonlinear operators.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The continuous-operator universal approximation theorem is inherited from Chen and Chen, not newly invented here; small approximation error does not bound optimization or generalization error. Original input sensor locations must be consistent across functions.
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People and systems
Lu Lu; Pengzhan Jin; George Em Karniadakis
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
DeepONet branch–trunk operator-learning architecture
Combines a branch network encoding sampled input functions with a trunk network encoding output locations, taking their learned feature inner product to approximate nonlinear operators.
Scientific scope
The continuous-operator universal approximation theorem is inherited from Chen and Chen, not newly invented here; small approximation error does not bound optimization or generalization error. Original input sensor locations must be consistent across functions.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Lu Lu; Pengzhan Jin; George Em Karniadakis
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.