Feb. 15, 2024, 5:43 a.m. | Gr\'egoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak T. Kiani

cs.LG updates on arXiv.org arxiv.org

arXiv:2307.05432v2 Announce Type: replace
Abstract: Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering. Though current algorithms typically require simulated training data tailored to a given setting, one may instead wish to learn useful information from heterogeneous sources, or from real dynamical systems observations that are messy or incomplete. In this work, we learn general-purpose representations of PDEs from heterogeneous data by implementing joint embedding methods …

arxiv cs.lg cs.na differential math.na self-supervised learning supervised learning type

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