Operator Boosting Produces Pareto-Efficient PDE Surrogates

In three linesOperator Boosting constructs compact neural-operator surrogates for PDEs via stagewise residual learning. Tested on FNO, DeepONet, and CNO across 30 benchmarks (PDEBench, APEBench), the method reduces parameters by 72–95% while improving accuracy on 21 dataset-architecture pairs and achieves Pareto gains on 7/10 PDE benchmarks.

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