June 7, 2024, 4:42 a.m. | Tian Wang, Chuang Wang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03923v1 Announce Type: new
Abstract: Neural operators effectively solve PDE problems from data without knowing the explicit equations, which learn the map from the input sequences of observed samples to the predicted values. Most existed works build the model in the original geometric space, leading to high computational costs when the number of sample points is large. We present the Latent Neural Operator (LNO) solving PDEs in the latent space. In particular, we first propose Physics-Cross-Attention (PhCA) transforming representation from …

abstract arxiv build computational costs cs.lg cs.na data input learn map math.na operators samples solve space type values

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