June 7, 2024, 4:42 a.m. | Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp, Mathias Niepert

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03919v1 Announce Type: new
Abstract: Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of Transformers, such as quadratic memory and time complexity. Furthermore, all prevalent architectures for PDE solving lack at least one of several desirable properties of an ideal surrogate model, such as (i) generalization to PDE parameters not seen during training, (ii) spatial and temporal zero-shot super-resolution, (iii) continuous temporal extrapolation, (iv) …

abstract architectures arxiv complexity cs.ai cs.cv cs.lg cs.ne differential fields framework memory parametric physics.comp-ph transformer transformer models transformers type

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