Web: http://arxiv.org/abs/2110.10249

Jan. 31, 2022, 2:11 a.m. | Cristopher Salvi, Maud Lemercier, Andris Gerasimovics

cs.LG updates on arXiv.org arxiv.org

Stochastic partial differential equations (SPDEs) are the mathematical tool
of choice for modelling spatiotemporal PDE-dynamics under the influence of
randomness. Based on the notion of mild solution of an SPDE, we introduce a
novel neural architecture to learn solution operators of PDEs with (possibly
stochastic) forcing from partially observed data. The proposed Neural SPDE
model provides an extension to two popular classes of physics-inspired
architectures. On the one hand, it extends Neural CDEs and variants --
continuous-time analogues of RNNs …

arxiv learning neural stochastic

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