November 8, 2025Open Access

Operator Learning: A Statistical Perspective

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Authors

USUnique SubediUniversity of Michigan ATAmbuj TewariUniversity of Michigan

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Implication

Analysis shows surrogate models improve solutions of partial differential equations using experimental data, indicating enhanced predictive capabilities.

Key Points

Operator learning improves predictions in infinite-dimensional function spaces, yielding more accurate approximations.
Surrogate models were developed for effective solutions in partial differential equations, using experimental data to enhance outcomes.
Formalization of operator learning is presented as a function-to-function regression problem for system behavior modeling.
Highlights future avenues in uncertainty quantification frameworks and active data collection methods for improved model reliability.

Cite This Study

Subedi et al. (2025) studied this question.

synapsesocial.com/papers/690e8b75a5b062d7a4e7379d https://doi.org/10.48550/arxiv.2504.03503

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Implication

Analysis shows surrogate models improve solutions of partial differential equations using experimental data, indicating enhanced predictive capabilities.

Operator Learning: A Statistical Perspective

Discussion

Member takes

Implication

Key Points

Cite This Study

Operator Learning: A Statistical Perspective

Discussion

Member takes

Implication

Key Points

Cite This Study

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