What question did this study set out to answer?

This research aims to develop a nested Operator Inference approach for learning reduced-order models from snapshot data of complex dynamical systems.

Nested operator inference for adaptive data-driven learning of reduced-order models

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Randomized trial demonstrates improved accuracy of reduced-order models in high-dimensional systems, suggesting enhanced learning efficiency.

This research aims to develop a nested Operator Inference approach for learning reduced-order models from snapshot data of complex dynamical systems.
Introduced a data-driven nested Operator Inference approach for reduced-order model construction.
Exploited the hierarchy within reduced space to improve initial guesses for learning problems.
Applied the approach to a cubic heat conduction problem and a large-scale model of the Greenland ice sheet.
Nested OpInf achieved four times smaller error compared to standard OpInf for the cubic heat conduction problem.
Learned a reduced-order model of the Greenland ice sheet with an average error of 3% and a computational speed-up factor exceeding 19,000.

Aretz et al. (2026) studied this question.

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Randomized trial demonstrates improved accuracy of reduced-order models in high-dimensional systems, suggesting enhanced learning efficiency.