Operator Boosting Produces Pareto-Efficient PDE Surrogates
Neural operators are widely used as surrogate solution maps for partial differential equations (PDEs), but full-size models can be costly to store, deploy, and evaluate in many-query scientific workflows. This work introduces Operator Boosting, a stagewise residual-learning framework for constructing compact neural-operator surrogates directly, rather than training a large model and compressing it afterward. Starting from the empirical mean predictor in normalized output coordinates, the method trains a sequence of tiny same-family neural operators on residual fields and incorporates each correction through validation-selected shrinkage. We instantiate the framework with Fourier neural op...
| Authors | Lennon J. Shikhman |
|---|---|
| Primary category | cs.LG |
| Published | 16 June 2026 |
| Last updated at source | 24 June 2026 |
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