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Neural Scaling Laws of Transformer Operator Network

arXiv:2609.33533v1 Announce Type: new Abstract: Transformers have emerged as powerful architectures for learning solution operators of physical systems. Empirically the prediction error has been observed to decrease when the data size and model size increase, suggesting neural scaling behavior. Yet…

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  1. 2026-09-29 04:00 · arXiv stat.ML
    Neural Scaling Laws of Transformer Operator Network

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