DeepONet branch–trunk operator-learning architecture

What changed

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

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. 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.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 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.

Contributors

• Source-listed authors and technical contributors · SCIENCE: 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.

• DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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