Across four PDE benchmarks, transfer learning from low-fidelity weights is the only multi-fidelity neural operator strategy that consistently outperforms the high-fidelity-only baseline, while direct LF-input methods degrade as the fidelity gap grows.
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Benchmarking Multi-fidelity Neural Operators on Complex PDE Problems with Non-trivial Fidelity Differences
Across four PDE benchmarks, transfer learning from low-fidelity weights is the only multi-fidelity neural operator strategy that consistently outperforms the high-fidelity-only baseline, while direct LF-input methods degrade as the fidelity gap grows.