Function graph transformers use graph measures to provide a measure-theoretic framework where standard transformer components universally approximate operators between function spaces while preserving single-valued function outputs.
and Fournier, John J
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
HyCOP learns policies over compositions of hybrid modules to produce interpretable programs for parametric PDE solution operators with order-of-magnitude OOD gains over monolithic neural operators.
citing papers explorer
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Function graph transformers universally approximate operators between function spaces
Function graph transformers use graph measures to provide a measure-theoretic framework where standard transformer components universally approximate operators between function spaces while preserving single-valued function outputs.
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HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs
HyCOP learns policies over compositions of hybrid modules to produce interpretable programs for parametric PDE solution operators with order-of-magnitude OOD gains over monolithic neural operators.