operator-boosting

Operator Boosting is a stagewise residual-learning framework that constructs compact neural operator surrogates for PDEs by training tiny models on residual fields. It achieves accuracy comparable to or better than full-size models while reducing parameters by up to 95%, demonstrating Pareto improvements on several benchmarks.