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October 19, 2025Open Access

RONOM: Reduced-Order Neural Operator Modeling

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This work presents RONOM, a new framework bridging reduced-order modeling and operator learning, highlighting its discretization error bounds.

RONOM offers improved spatial super-resolution while maintaining robust discretization across varying meshes.
In two numerical examples, RONOM achieved comparable results with existing methods but excelled in input generalization.
This framework establishes a new discretization error bound, enhancing flexibility in real-time forecasting and control.
Insights into both temporal and spatial aspects of super-resolution were gained, benefiting applications in uncertain scenarios.

Dummer et al. (2025) studied this question.

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This work presents RONOM, a new framework bridging reduced-order modeling and operator learning, highlighting its discretization error bounds.