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Spectral disentanglement and knowledge-guided aggregation unlock efficient, high-performance MoE neural operators for large-scale PDE pretraining, slashing expert count by 50% without sacrificing accuracy.
A nested Mixture-of-Experts architecture lets neural operators pre-trained on diverse PDEs transfer more effectively to downstream tasks.
