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%2C a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs from various domains%2C dimensions%2C and resolutions. UPS embeds different PDEs into a shared representation space and processes them using a FNO-transformer architecture. Rather than training the network from scratch%2C which is data-demanding and computationally expensive%2C we warm-start the transformer from pretrained LLMs and perform explicit alignment to reduce the modality gap while improving data and compute efficiency. The cross-modal UPS achieves state-of-the-art results on a wide range of 1D and 2D PDE families from PDEBench%2C outperforming existing unified models using 4 times less data and 26 times less compute. Meanwhile%2C it is capable of few-shot transfer to unseen PDE families and coefficients.?quality=80&w=800)
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